From 9b6177d2a7e55627cf52da46d766ede9c268d8ec Mon Sep 17 00:00:00 2001 From: Julius Ruschenpohler <45004123+ruschenpohler@users.noreply.github.com> Date: Wed, 3 Jun 2026 22:07:55 +0800 Subject: [PATCH 1/5] feat: Phase 1 core model - Intervention, league table, visualization [7] --- .gitignore | 1 - notebooks/01_core_model.ipynb | 135 ++++++++ pyproject.toml | 9 +- src/cea_dev/core.py | 87 +++++ src/cea_dev/visualization.py | 55 +++ tests/test_core.py | 219 ++++++++++++ uv.lock | 607 ++++++++++++++++++++++++++++++++++ 7 files changed, 1110 insertions(+), 3 deletions(-) create mode 100644 notebooks/01_core_model.ipynb create mode 100644 src/cea_dev/core.py create mode 100644 src/cea_dev/visualization.py create mode 100644 tests/test_core.py diff --git a/.gitignore b/.gitignore index 9b7ffc0..36aff84 100644 --- a/.gitignore +++ b/.gitignore @@ -27,7 +27,6 @@ credentials.json # notebooks .ipynb_checkpoints/ -notebooks/ # spark metastore_db/ diff --git a/notebooks/01_core_model.ipynb b/notebooks/01_core_model.ipynb new file mode 100644 index 0000000..bcefaa3 --- /dev/null +++ b/notebooks/01_core_model.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Phase 1 — The Core Model\n", + "\n", + "**Goal:** Define the cost-effectiveness ratio, introduce the internal metric $v_i$ (SD per dollar), and build the unadjusted league table. \n", + "No uncertainty, no time dimension. Everything is point estimates.\n", + "\n", + "## Key concepts\n", + "\n", + "**Internal metric (effectiveness):** $v_i = \\frac{\\hat{\\tau}_i}{C_i}$ \n", + "where $\\hat{\\tau}_i$ is the estimated effect in SD and $C_i$ is cost per beneficiary in USD.\n", + "\n", + "**Display metric (CE ratio):** $CE_i^* = \\frac{0.1}{v_i} = \\frac{0.1 \\cdot C_i}{\\hat{\\tau}_i}$ \n", + "This is read as \\\"cost per 0.1 SD of learning gain.\\\"\n", + "\n", + "**Assumptions active in Phase 1:**\n", + "- **A0:** Effect estimate is known (no sampling uncertainty)\n", + "- **A1:** Costs are proportional to beneficiaries (no fixed costs)\n", + "- **A2:** One-year time horizon\n", + "- **A3:** No effect decay\n", + "\n", + "Note: The full `Intervention` dataclass includes fields for fixed costs, durations, and decay rates (with defaults that make them inactive), so the data schema is forward-compatible with Phases 2–5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from cea_dev.core import Intervention, InterventionSet\n", + "from cea_dev.visualization import plot_league_table\n", + "\n", + "pd.set_option(\"display.precision\", 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the synthetic intervention data\n", + "\n", + "The eight education interventions are parameterized from published evaluation ranges. All cost bounds are imputed at $\\pm 20\\%$ of the point estimate (cost_imputed = True for all)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = Path(\"../data\")\n", + "iset = InterventionSet.from_csv(str(DATA_DIR / \"interventions.csv\"))\n", + "print(f\"Loaded {len(iset.interventions)} interventions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unadjusted league table\n", + "\n", + "Ranked by effectiveness $v_i$ (SD per dollar) descending. The `ce_display` column is cost per 0.1 SD — lower is better." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "table = iset.league_table()\n", + "table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## League table chart\n", + "\n", + "Horizontal bar chart sorted by effectiveness. Cost per 0.1 SD on x-axis (log scale). Color-coded by quartile:\n", + "- **Best Buy:** top quartile (lowest CE*)\n", + "- **Good Buy:** second quartile\n", + "- **Promising:** third quartile\n", + "- **Weak:** bottom quartile (highest CE*)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plot_league_table(table)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes\n", + "\n", + "1. **TaRL ranks first** with effectiveness $v = 0.18 / 4.00 = 0.045$ SD per dollar ($CE^* = \\$2.22$ per 0.1 SD).\n", + "2. **CCT ranks last** with $v = 0.07 / 22.00 \\approx 0.0032$ SD per dollar ($CE^* = \\$31.43$ per 0.1 SD).\n", + "3. **ECD appears mid-table** ($v = 0.0088$, rank 5) despite the largest effect (0.22 SD) because its cost is high (\\$25/beneficiary). Its ranking will improve materially in Phase 2 when its 10-year duration is accounted for.\n", + "4. **Deworming ranks second** ($v = 0.025$) due to its extremely low cost (\\$2/beneficiary) despite a small effect (0.05 SD). However, its SE/effect ratio (0.80) means its ranking is fragile — Phase 3 will show $P(\\text{harm}) > 5\\%$.\n", + "5. **The ranking is provisional.** All four assumptions (A0–A3) will be relaxed in subsequent phases." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 6f2f30b..c6231d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,12 @@ name = "cea-dev" version = "0.1.0" requires-python = ">=3.11,<4.0" -dependencies = [] +dependencies = [ + "matplotlib>=3.10.9", + "numpy>=2.4.6", + "pandas>=3.0.3", + "seaborn>=0.13.2", +] [project.optional-dependencies] dev = [ @@ -23,4 +28,4 @@ exclude = ["src/legacy/"] select = ["E", "F", "I"] [tool.pytest.ini_options] -testpaths = ["tests"] \ No newline at end of file +testpaths = ["tests"] diff --git a/src/cea_dev/core.py b/src/cea_dev/core.py new file mode 100644 index 0000000..f0212f1 --- /dev/null +++ b/src/cea_dev/core.py @@ -0,0 +1,87 @@ +from dataclasses import MISSING, dataclass, fields + +import pandas as pd + + +@dataclass +class Intervention: + name: str + effect_sd: float # ASSUMPTION A0: effect is known + effect_se: float + cost_est: float + cost_low: float + cost_high: float + cost_imputed: bool = False + fixed_cost: float = 0.0 # ASSUMPTION A1: proportional costs (no fixed) + variable_cost_per_participant: float = 0.0 + compliance_rate: float = 1.0 + n_studies: int = 1 + duration_years: int = 1 # ASSUMPTION A2: default 1-year horizon + annual_decay_rate: float = 1.0 # ASSUMPTION A3: no decay by default + discount_rate: float = 0.03 + + @property + def effectiveness(self) -> float: + r"""SD per dollar (internal metric). $v_i = \hat{\tau}_i / C_i$""" + return self.effect_sd / self.cost_est + + @property + def ce_display(self) -> float: + r"""Cost per 0.1 SD (display metric). $CE_i^* = 0.1 / v_i$""" + return 0.1 / self.effectiveness + + +@dataclass +class InterventionSet: + interventions: list[Intervention] + + def league_table(self) -> pd.DataFrame: + df = pd.DataFrame( + [ + { + "name": iv.name, + "effect_sd": iv.effect_sd, + "cost_est": iv.cost_est, + "effectiveness": iv.effectiveness, + "ce_display": iv.ce_display, + } + for iv in self.interventions + ] + ) + df = df.sort_values("effectiveness", ascending=False).reset_index(drop=True) + df.index = df.index + 1 + df.index.name = "rank" + return df + + @classmethod + def from_csv(cls, path: str) -> "InterventionSet": + df = pd.read_csv(path) + required = { + "name", + "effect_sd", + "effect_se", + "cost_est", + "cost_low", + "cost_high", + } + missing = required - set(df.columns) + if missing: + raise ValueError(f"Missing required columns in CSV: {missing}") + + interventions = [] + for _, row in df.iterrows(): + kwargs = { + col: row[col] for col in df.columns if col in _INTERVENTION_FIELDS + } + for field_name, field_default in _INTERVENTION_DEFAULTS.items(): + if field_name not in kwargs: + kwargs[field_name] = field_default + interventions.append(Intervention(**kwargs)) + return cls(interventions) + + +_INTERVENTION_FIELDS = {f.name for f in fields(Intervention) if f.init} + +_INTERVENTION_DEFAULTS = { + f.name: f.default for f in fields(Intervention) if f.default is not MISSING +} diff --git a/src/cea_dev/visualization.py b/src/cea_dev/visualization.py new file mode 100644 index 0000000..2f120f9 --- /dev/null +++ b/src/cea_dev/visualization.py @@ -0,0 +1,55 @@ +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + +_QUARTILE_LABELS = ["Best Buy", "Good Buy", "Promising", "Weak"] +_QUARTILE_COLORS = ["#0072B2", "#56B4E9", "#E69F00", "#D55E00"] + + +def plot_league_table( + league: pd.DataFrame, + title: str = "Cost-Effectiveness League Table", + figsize: tuple[float, float] = (9, 5), +) -> tuple[plt.Figure, plt.Axes]: + df = league.copy() + n = len(df) + + quartile_idx = pd.cut( + np.arange(n), + bins=4, + labels=False, + ) + bar_colors = [_QUARTILE_COLORS[int(q)] for q in quartile_idx] + + y_positions = list(reversed(range(n))) + names = list(reversed(df["name"].values)) + ce_values = list(reversed(df["ce_display"].values)) + + fig, ax = plt.subplots(figsize=figsize) + bars = ax.barh(y_positions, ce_values, color=bar_colors, edgecolor="white") + ax.set_yticks(y_positions) + ax.set_yticklabels(names) + ax.set_xlabel("Cost per 0.1 SD (USD, log scale)") + ax.set_xscale("log") + ax.set_title(title) + ax.invert_yaxis() + + for i, (bar, value) in enumerate(zip(bars, ce_values)): + ax.text( + value * 1.02, + bar.get_y() + bar.get_height() / 2, + f"${value:.2f}", + va="center", + fontsize=9, + ) + + legend_handles = [ + plt.Rectangle((0, 0), 1, 1, color=c, label=label) + for c, label in zip(_QUARTILE_COLORS, _QUARTILE_LABELS) + ] + ax.legend(handles=legend_handles, loc="lower right", fontsize=8) + + sns.despine() + fig.tight_layout() + return fig, ax diff --git a/tests/test_core.py b/tests/test_core.py new file mode 100644 index 0000000..757b16b --- /dev/null +++ b/tests/test_core.py @@ -0,0 +1,219 @@ +import pandas as pd +import pytest + +from cea_dev.core import Intervention, InterventionSet + + +def _make_interventions() -> list[Intervention]: + return [ + Intervention( + name="A", + effect_sd=0.20, + effect_se=0.05, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + ), + Intervention( + name="B", + effect_sd=0.10, + effect_se=0.03, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + ), + Intervention( + name="C", + effect_sd=0.05, + effect_se=0.02, + cost_est=1.0, + cost_low=0.8, + cost_high=1.2, + ), + ] + + +class TestIntervention: + def test_effectiveness_is_sd_per_dollar(self): + iv = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + ) + assert iv.effectiveness == pytest.approx(0.05) + + def test_ce_display_inverts_and_scales(self): + iv = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + ) + assert iv.ce_display == pytest.approx(2.0) + + def test_ce_display_roundtrip(self): + iv = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + ) + assert iv.ce_display == pytest.approx(0.1 / iv.effectiveness) + + def test_higher_cost_worsens_ce(self): + cheap = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=1.0, + cost_low=0.8, + cost_high=1.2, + ) + expensive = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + ) + assert cheap.effectiveness > expensive.effectiveness + + def test_larger_effect_improves_ce(self): + small = Intervention( + name="X", + effect_sd=0.05, + effect_se=0.02, + cost_est=1.0, + cost_low=0.8, + cost_high=1.2, + ) + large = Intervention( + name="X", + effect_sd=0.20, + effect_se=0.02, + cost_est=1.0, + cost_low=0.8, + cost_high=1.2, + ) + assert large.effectiveness > small.effectiveness + + def test_compliance_invariance_under_proportional_costs(self): + a = Intervention( + name="A", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + fixed_cost=0.0, + compliance_rate=1.0, + ) + b = Intervention( + name="A", + effect_sd=0.10, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + fixed_cost=0.0, + compliance_rate=0.80, + ) + assert a.effectiveness == pytest.approx(b.effectiveness) + + def test_default_fields(self): + iv = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=1.0, + cost_low=0.8, + cost_high=1.2, + ) + assert iv.fixed_cost == 0.0 + assert iv.compliance_rate == 1.0 + assert iv.duration_years == 1 + assert iv.annual_decay_rate == 1.0 + assert iv.discount_rate == 0.03 + + +class TestInterventionSet: + def test_league_table_sorted_by_effectiveness(self): + interventions = _make_interventions() + iset = InterventionSet(interventions) + table = iset.league_table() + eff = table["effectiveness"].values + assert all(eff[i] >= eff[i + 1] for i in range(len(eff) - 1)) + + def test_league_table_has_expected_columns(self): + interventions = _make_interventions() + iset = InterventionSet(interventions) + table = iset.league_table() + assert set(table.columns) >= { + "name", + "effect_sd", + "cost_est", + "effectiveness", + "ce_display", + } + + def test_league_table_rank_starts_at_one(self): + interventions = _make_interventions() + iset = InterventionSet(interventions) + table = iset.league_table() + assert table.index.name == "rank" + assert table.index[0] == 1 + + def test_roundtrip_from_csv(self, tmp_path): + interventions = _make_interventions() + source = InterventionSet(interventions) + table_before = source.league_table() + + csv_path = tmp_path / "test_interventions.csv" + df = pd.DataFrame([vars(iv) for iv in interventions]) + df["name"] = df["name"].astype(str) + df.to_csv(csv_path, index=False) + + loaded = InterventionSet.from_csv(str(csv_path)) + table_after = loaded.league_table() + + pd.testing.assert_frame_equal( + table_before.reset_index(drop=True), + table_after.reset_index(drop=True), + ) + + def test_ranking_invariant_to_cost_scaling(self): + base = _make_interventions() + iset_base = InterventionSet(base) + ranks_base = list(iset_base.league_table()["name"]) + + factor = 2.5 + scaled = [ + Intervention( + name=iv.name, + effect_sd=iv.effect_sd, + effect_se=iv.effect_se, + cost_est=iv.cost_est * factor, + cost_low=iv.cost_low * factor, + cost_high=iv.cost_high * factor, + ) + for iv in base + ] + iset_scaled = InterventionSet(scaled) + ranks_scaled = list(iset_scaled.league_table()["name"]) + + assert ranks_base == ranks_scaled + + def test_from_csv_missing_columns_raises(self, tmp_path): + df = pd.DataFrame({"name": ["X"], "effect_sd": [0.1]}) + path = tmp_path / "bad.csv" + df.to_csv(path, index=False) + with pytest.raises(ValueError, match="Missing required columns"): + InterventionSet.from_csv(str(path)) diff --git a/uv.lock b/uv.lock index e55a7c9..38ccd35 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,25 @@ version = 1 revision = 3 requires-python = ">=3.11, <4.0" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] [[package]] name = "cea-dev" version = "0.1.0" source = { editable = "." } +dependencies = [ + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pandas" }, + { name = "seaborn" }, +] [package.optional-dependencies] dev = [ @@ 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src/cea_dev/time_horizon.py | 4 ++ src/cea_dev/uncertainty.py | 4 ++ 5 files changed, 139 insertions(+) create mode 100644 README.md create mode 100644 src/cea_dev/psa.py create mode 100644 src/cea_dev/sensitivity.py create mode 100644 src/cea_dev/time_horizon.py create mode 100644 src/cea_dev/uncertainty.py diff --git a/README.md b/README.md new file mode 100644 index 0000000..c86e790 --- /dev/null +++ b/README.md @@ -0,0 +1,125 @@ +# Cost-Effectiveness Analysis in Development Economics + +## 1. What is CEA? + +The decision problem: a budget, multiple candidates, one outcome. CEA — cost-effectiveness analysis — answers the question: given a fixed budget, which intervention produces the largest total outcome? It expresses value as _cost per unit of outcome achieved_. + +CEA vs. CBA: cost-benefit analysis monetizes all outcomes; CEA compares within a single natural-unit outcome domain. No moral weights, no income-doubling equivalents. (See Appendix A: Glossary.) + +The core principle: no intervention is cost-effective in isolation. Cost-effectiveness is a ranking. An intervention looks cheap or expensive only relative to a comparator. + +--- + +## 2. The Worked Example: Eight Education Interventions + +**Outcome:** Improvement in standardized test scores, measured in standard deviations (SD). The display metric is _cost per 0.1 SD gain per beneficiary_. + +All parameters are synthetic but calibrated to plausible ranges from published evaluations in the education literature. The eight interventions: + +| Intervention | Effect (SD) | Cost/ben. ($) | Duration (yrs) | +|---|---|---|---| +| Teaching at the Right Level (TaRL) | 0.18 | 4.00 | 2 | +| Structured pedagogy | 0.12 | 8.00 | 2 | +| Computer-assisted learning (CAL) | 0.14 | 12.00 | 3 | +| School meals | 0.09 | 15.00 | 1 | +| Early childhood development (ECD) | 0.22 | 25.00 | 10 | +| Teacher performance pay | 0.10 | 18.00 | 2 | +| Conditional cash transfer (CCT) | 0.07 | 22.00 | 3 | +| Deworming | 0.05 | 2.00 | 1 | + +Full parameter table with standard errors, compliance rates, and cost bounds is in `data/interventions.csv`. + +### What this analysis can and cannot tell you + +The cost-effectiveness ratios in this framework are computed from effect estimates and cost figures reported in evaluation studies. Effect estimates carry sampling uncertainty — the standard error reflects the fact that the study observed one sample from a population, and a different sample would have produced a different estimate. This uncertainty propagates directly into rankings: an intervention that looks like the best buy at point estimates may not look like the best buy once we account for the fact that its effect estimate is imprecise. We quantify and visualize this throughout. + +Three further sources of uncertainty exist but cannot be quantified from published data alone. The first is external validity: whether the effect observed in the study population replicates in the target context, which requires multiple independent replications with documented context variation. The second is the counterfactual: whether the target population would receive a comparable intervention in the absence of this one, which requires data on take-up of comparable programs in the target setting. The third is implementation fidelity at scale: whether the effect holds when moving from a supervised research trial to routine government delivery, which requires paired efficacy and effectiveness evaluations of the same protocol. Published evaluations rarely report the data needed to estimate any of these three factors systematically. The closest existing practice is GiveWell's approach of assigning explicit but judgment-based weights to each. Rankings produced by this framework should be read as conditional on the study estimates: informative about relative cost-effectiveness under the assumption that effects replicate, and a starting point for the harder judgment about whether they will. + +--- + +## 3. The Core Model [→ notebook 01] + +**Internal metric:** effectiveness, defined as SD of learning gain per dollar: + +$$v_i = \frac{\hat{\tau}_i}{C_i}$$ + +where $\hat{\tau}_i$ is the estimated effect in SD and $C_i$ is cost per beneficiary in USD. For display, this is inverted and scaled to cost per 0.1 SD: + +$$CE_i^* = \frac{0.1}{v_i} = \frac{0.1 \cdot C_i}{\hat{\tau}_i}$$ + +Working in $v_i$ throughout avoids numerical instability when $\hat{\tau}_i$ is near zero. All uncertainty intervals are computed on the $v_i$ scale and inverted for display; note that this inversion makes confidence intervals asymmetric in the $CE^*$ display metric, which is correct and should not be adjusted away. + +### ITT vs. TOT and cost structure + +In a randomized controlled trial (RCT), the intent-to-treat (ITT) estimate $\hat{\tau}_{ITT}$ is the average effect over all individuals offered the intervention, including non-compliers. The treatment-on-the-treated (TOT) estimate $\hat{\tau}_{TOT} = \hat{\tau}_{ITT} / p_c$ isolates the effect on those who actually participated, where $p_c$ is the compliance rate. Under the simplifying assumption that total cost scales proportionally with the number of individuals offered the intervention (**Assumption A1: proportional costs**), the CE ratio is invariant to whether ITT or TOT is used, because the compliance rate cancels. This assumption is relaxed in Section 4. + +Invariance holds only when all costs are variable; that is, when total program cost scales proportionally with the number of people offered the intervention. Fixed costs, such as curriculum development, staff training, and platform infrastructure, violate this assumption. When fixed costs are material, low compliance spreads them over fewer effective participants, making the cost per complier higher than the cost per person offered. As a consequence, ITT may understate the true cost of achieving a particular effect. Section 4 relaxes the proportional-cost assumption and shows how the CE ratio changes. + +### Cost reporting + +We use the cost estimate reported in each source study. Studies vary in what their cost estimates include — implementation overheads, volunteer time, household costs, and amortization of capital expenditure are treated inconsistently across the literature. Costs are often reported without component detail. This limits comparability and is a documented constraint of the framework, not a modeling choice. + +### A note on time + +The league table below assumes that effects persist for exactly one year and do not decay thereafter. ECD and CAL produce their effects over longer horizons; school meals and deworming do not. Section 4 introduces heterogeneous durations and annual decay rates, and the ranking shifts materially. + +### The unadjusted league table + +| Rank | Intervention | Effect (SD) | Cost ($) | Effectiveness (SD/$) | Cost per 0.1 SD ($) | +|---|---|---|---|---|---| +| 1 | Teaching at the Right Level (TaRL) | 0.18 | 4.00 | 0.0450 | 2.22 | +| 2 | Deworming | 0.05 | 2.00 | 0.0250 | 4.00 | +| 3 | Structured pedagogy | 0.12 | 8.00 | 0.0150 | 6.67 | +| 4 | Computer-assisted learning (CAL) | 0.14 | 12.00 | 0.0117 | 8.57 | +| 5 | Early childhood development (ECD) | 0.22 | 25.00 | 0.0088 | 11.36 | +| 6 | School meals | 0.09 | 15.00 | 0.0060 | 16.67 | +| 7 | Teacher performance pay | 0.10 | 18.00 | 0.0056 | 18.00 | +| 8 | Conditional cash transfer (CCT) | 0.07 | 22.00 | 0.0032 | 31.43 | + +See `notebooks/01_core_model.ipynb` for the computational version and chart. + +--- + +## 4. Adjustments: Time Horizon and Discounting [→ notebook 02] + +*(To be implemented in Phase 2.)* + +--- + +## 5. Uncertainty [→ notebook 03] + +*(To be implemented in Phase 3.)* + +--- + +## 6. Sensitivity Analysis [→ notebook 04] + +*(To be implemented in Phase 4.)* + +--- + +## 7. Joint Sensitivity Analysis [→ notebook 05] + +*(To be implemented in Phase 5.)* + +--- + +## 8. Extension: Bayesian CEA [→ extensions/06_bayesian.ipynb] + +*(To be implemented in Phase 6.)* + +--- + +## Appendix A: Glossary + +- **CEA (cost-effectiveness analysis):** Comparison of interventions within a single outcome domain using cost per unit of outcome. +- **CBA (cost-benefit analysis):** Comparison that monetizes all outcomes and compares benefits to costs. +- **Systematic review:** A structured synthesis of all available evidence on a specific question. +- **Meta-analysis:** A statistical combination of effect estimates from multiple studies. +- **ITT (intent-to-treat):** Effect estimated over all individuals offered the intervention, regardless of compliance. +- **TOT (treatment-on-the-treated):** Effect estimated over individuals who actually received the intervention. +- **P(harm):** Probability that an intervention's true effect is zero or negative. + +## Appendix B: Mathematical Details + +*(To be populated as phases are completed.)* diff --git a/src/cea_dev/psa.py b/src/cea_dev/psa.py new file mode 100644 index 0000000..b034c60 --- /dev/null +++ b/src/cea_dev/psa.py @@ -0,0 +1,3 @@ +"""Probabilistic / joint sensitivity analysis module. +Phase 5: Joint variation over all uncertain parameters. Not yet implemented. +""" diff --git a/src/cea_dev/sensitivity.py b/src/cea_dev/sensitivity.py new file mode 100644 index 0000000..505f98f --- /dev/null +++ b/src/cea_dev/sensitivity.py @@ -0,0 +1,3 @@ +"""Sensitivity analysis module. +Phase 4: One-way sensitivity analysis and tornado plots. Not yet implemented. +""" diff --git a/src/cea_dev/time_horizon.py b/src/cea_dev/time_horizon.py new file mode 100644 index 0000000..3c65b55 --- /dev/null +++ b/src/cea_dev/time_horizon.py @@ -0,0 +1,4 @@ +"""Time horizon and discounting module. +Phase 2: Relaxes Assumptions A1 (proportional costs), A2 (one-year horizon), +and A3 (no decay). Not yet implemented. +""" diff --git a/src/cea_dev/uncertainty.py b/src/cea_dev/uncertainty.py new file mode 100644 index 0000000..fdc5728 --- /dev/null +++ b/src/cea_dev/uncertainty.py @@ -0,0 +1,4 @@ +"""Bootstrap uncertainty module. +Phase 3: Relaxes Assumption A0 (effect is known). Parametric and non-parametric +bootstrap over effect standard errors. Not yet implemented. +""" From f29f2910e9ea6c3ab17b6783f8cdf57a2d07a1e8 Mon Sep 17 00:00:00 2001 From: Julius Ruschenpohler <45004123+ruschenpohler@users.noreply.github.com> Date: Wed, 3 Jun 2026 22:52:48 +0800 Subject: [PATCH 3/5] chore: add extensions/ directory per Phase 0 repo structure [1] --- extensions/.gitkeep | 1 + 1 file changed, 1 insertion(+) create mode 100644 extensions/.gitkeep diff --git a/extensions/.gitkeep b/extensions/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/extensions/.gitkeep @@ -0,0 +1 @@ + From 69864c6b6fa473a7898d565814864326ed648ecc Mon Sep 17 00:00:00 2001 From: Julius Ruschenpohler <45004123+ruschenpohler@users.noreply.github.com> Date: Wed, 3 Jun 2026 23:00:39 +0800 Subject: [PATCH 4/5] feat: Phase 2 - time horizon, discounting, fixed/variable costs relaxes A1-A3 [7] --- notebooks/02_time_horizon.ipynb | 177 +++++++++++++++++++++ src/cea_dev/time_horizon.py | 123 ++++++++++++++- src/cea_dev/visualization.py | 61 ++++++++ tests/test_time_horizon.py | 262 ++++++++++++++++++++++++++++++++ 4 files changed, 622 insertions(+), 1 deletion(-) create mode 100644 notebooks/02_time_horizon.ipynb create mode 100644 tests/test_time_horizon.py diff --git a/notebooks/02_time_horizon.ipynb b/notebooks/02_time_horizon.ipynb new file mode 100644 index 0000000..b9ee416 --- /dev/null +++ b/notebooks/02_time_horizon.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Phase 2 — Time Horizon and Discounting\n", + "\n", + "**Goal:** Relax three assumptions — A1 (proportional costs), A2 (one-year horizon), and A3 (no decay) — and show that time-adjusted rankings differ materially from the Phase 1 baseline.\n", + "\n", + "## Assumptions relaxed\n", + "\n", + "- **A1 (proportional costs):** Phase 1 assumed costs scale proportionally with beneficiaries. We now decompose costs into fixed ($C_i^{fixed}$) and variable ($C_i^{var}$) components, and adjust for compliance rate $p_c$.\n", + "- **A2 (one-year horizon):** Phase 1 assumed all interventions produce effects over exactly one year. We now use heterogeneous durations $T_i$.\n", + "- **A3 (no decay):** Phase 1 assumed effects persist at full strength indefinitely. We now model exponential decay with annual retention rate $\\rho_i$.\n", + "\n", + "## Key formulas\n", + "\n", + "**Discounted effective effect:**\n", + "$$\\hat{\\tau}_i^{eff} = \\hat{\\tau}_i \\cdot \\sum_{t=0}^{T_i - 1} \\frac{\\rho_i^t}{(1+r)^t}$$\n", + "\n", + "**Adjusted cost:**\n", + "$$C_i^{adj} = \\frac{C_i^{fixed}}{N} + \\frac{C_i^{var}}{p_{c,i}}$$\n", + "\n", + "**Time-adjusted effectiveness:**\n", + "$$v_i^{adj} = \\frac{\\hat{\\tau}_i^{eff}}{C_i^{adj}}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from cea_dev.core import InterventionSet\n", + "from cea_dev.time_horizon import DiscountedInterventionSet\n", + "from cea_dev.visualization import plot_league_table, plot_ranking_shift\n", + "\n", + "pd.set_option(\"display.precision\", 4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data and build Phase 1 baseline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = Path(\"../data\")\n", + "iset = InterventionSet.from_csv(str(DATA_DIR / \"interventions.csv\"))\n", + "p1_table = iset.league_table()\n", + "print(\"Phase 1 — Unadjusted league table:\")\n", + "p1_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build Phase 2 — Time-adjusted league table\n", + "\n", + "Using $N = 1{,}000$ beneficiaries (held constant across interventions for comparability)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "diset = DiscountedInterventionSet.from_intervention_set(iset)\n", + "p2_table = diset.league_table()\n", + "print(\"Phase 2 — Time-adjusted league table:\")\n", + "p2_table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Side-by-side comparison\n", + "\n", + "**Headline result:** ECD moves from rank 5 to rank 2 when its 10-year duration is accounted for. Short-duration interventions with low retention (school meals, deworming) lose ground." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shift = diset.ranking_shift(iset)\n", + "shift" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plot_ranking_shift(shift)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sensitivity to discount rate\n", + "\n", + "The plan specifies testing ECD's ranking robustness to discount rates in the range 2–6%. Here we check whether the broad pattern holds.\n", + "\n", + "(Full sensitivity analysis is developed in Phase 4.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rates = [0.02, 0.03, 0.04, 0.05, 0.06]\n", + "for r in rates:\n", + " temp_ivs = []\n", + " for iv in iset.interventions:\n", + " import copy\n", + " temp = copy.deepcopy(iv)\n", + " temp.discount_rate = r\n", + " temp_ivs.append(temp)\n", + " temp_iset = InterventionSet(temp_ivs)\n", + " temp_diset = DiscountedInterventionSet.from_intervention_set(temp_iset)\n", + " table = temp_diset.league_table()\n", + " ecd_row = table[table[\"name\"] == \"Early childhood development (ECD)\"]\n", + " print(f\"r = {r:.2f}: ECD rank = {ecd_row.index[0]}, CE* = ${ecd_row['ce_display'].values[0]:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notes\n", + "\n", + "1. **ECD moves from 5th to 2nd.** Its 10-year duration and 90% annual retention produce a cumulative effect multiplier of ~6.0×, more than offsetting its higher per-beneficiary cost. At $r = 3\\%$, ECD's time-adjusted effectiveness is ~5× its Phase 1 value.\n", + "2. **Deworming and school meals lose ground.** Both have single-year durations and steep decay, so they receive no benefit from the time adjustment while their adjusted costs increase slightly (fixed costs spread per beneficiary).\n", + "3. **TaRL holds first place.** Its 2-year duration and 80% retention give it a ~1.8× effect multiplier, and its costs remain low. It is the most robust intervention in the set.\n", + "4. **Rank shifts are sensitive to discount rate.** ECD's rank advantage narrows at higher discount rates because its long-duration payoff is discounted more heavily. But even at $r = 6\\%$, it remains in the top 3.\n", + "5. **Fixed costs matter at modest scale.** At $N = 1{,}000$, CAL's high fixed cost ($3{,}000) adds $3/beneficiary to its cost, noticeably degrading its CE ratio. At larger scale the fixed cost dilutes; at smaller scale it dominates. The choice of $N$ is therefore a sensitivity parameter (explored in Phase 4)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/cea_dev/time_horizon.py b/src/cea_dev/time_horizon.py index 3c65b55..cf60873 100644 --- a/src/cea_dev/time_horizon.py +++ b/src/cea_dev/time_horizon.py @@ -1,4 +1,125 @@ """Time horizon and discounting module. + Phase 2: Relaxes Assumptions A1 (proportional costs), A2 (one-year horizon), -and A3 (no decay). Not yet implemented. +and A3 (no decay). Introduces heterogeneous durations, exponential effect +decay, and fixed/variable cost decomposition. """ + +from dataclasses import dataclass + +import pandas as pd + +from cea_dev.core import Intervention, InterventionSet + +_DEFAULT_BENEFICIARIES = 1000 + + +@dataclass +class DiscountedIntervention: + """Wraps Intervention with time-adjusted effective effect and adjusted cost.""" + + intervention: Intervention + n_beneficiaries: int = _DEFAULT_BENEFICIARIES + + @property + def discounted_effect(self) -> float: + # RELAXING A2, A3: heterogeneous duration and decay + tau = self.intervention.effect_sd + rho = self.intervention.annual_decay_rate + r = self.intervention.discount_rate + T = self.intervention.duration_years + return tau * sum(rho**t / (1 + r) ** t for t in range(T)) + + @property + def adjusted_cost(self) -> float: + # RELAXING A1: non-zero fixed costs + fixed = self.intervention.fixed_cost + var = self.intervention.variable_cost_per_participant + p_c = self.intervention.compliance_rate + return fixed / self.n_beneficiaries + var / p_c + + @property + def effectiveness(self) -> float: + return self.discounted_effect / self.adjusted_cost + + @property + def ce_display(self) -> float: + return 0.1 / self.effectiveness + + +class DiscountedInterventionSet: + def __init__( + self, + interventions: list[DiscountedIntervention], + n_beneficiaries: int = _DEFAULT_BENEFICIARIES, + ): + self.interventions = interventions + self.n_beneficiaries = n_beneficiaries + + def league_table(self) -> pd.DataFrame: + df = pd.DataFrame( + [ + { + "name": di.intervention.name, + "effect_sd": di.intervention.effect_sd, + "discounted_effect": round(di.discounted_effect, 4), + "adjusted_cost": round(di.adjusted_cost, 2), + "effectiveness": di.effectiveness, + "ce_display": di.ce_display, + } + for di in self.interventions + ] + ) + df = df.sort_values("effectiveness", ascending=False).reset_index(drop=True) + df.index = df.index + 1 + df.index.name = "rank" + return df + + def ranking_shift(self, baseline: InterventionSet) -> pd.DataFrame: + p2 = self.league_table().reset_index() + p2["rank_phase2"] = p2["rank"] + p2 = p2[["name", "rank_phase2", "effectiveness", "ce_display"]] + p2 = p2.rename( + columns={ + "effectiveness": "effectiveness_p2", + "ce_display": "ce_display_p2", + } + ) + + p1 = baseline.league_table().reset_index() + p1["rank_phase1"] = p1["rank"] + p1 = p1[["name", "rank_phase1", "effectiveness", "ce_display"]] + p1 = p1.rename( + columns={ + "effectiveness": "effectiveness_p1", + "ce_display": "ce_display_p1", + } + ) + + merged = p1.merge(p2, on="name") + merged["rank_shift"] = merged["rank_phase2"] - merged["rank_phase1"] + merged = merged.sort_values("rank_phase2").reset_index(drop=True) + merged.index = merged.index + 1 + merged.index.name = "rank" + return merged[ + [ + "name", + "rank_phase1", + "rank_phase2", + "rank_shift", + "ce_display_p1", + "ce_display_p2", + ] + ] + + @classmethod + def from_intervention_set( + cls, + intervention_set: InterventionSet, + n_beneficiaries: int = _DEFAULT_BENEFICIARIES, + ) -> "DiscountedInterventionSet": + interventions = [ + DiscountedIntervention(iv, n_beneficiaries=n_beneficiaries) + for iv in intervention_set.interventions + ] + return cls(interventions, n_beneficiaries=n_beneficiaries) diff --git a/src/cea_dev/visualization.py b/src/cea_dev/visualization.py index 2f120f9..f9a8784 100644 --- a/src/cea_dev/visualization.py +++ b/src/cea_dev/visualization.py @@ -53,3 +53,64 @@ def plot_league_table( sns.despine() fig.tight_layout() return fig, ax + + +def plot_ranking_shift( + shift_df: pd.DataFrame, + figsize: tuple[float, float] = (14, 5), +) -> tuple[plt.Figure, tuple[plt.Axes, plt.Axes]]: + df = shift_df.copy() + n = len(df) + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize, sharey=True) + + y_positions = list(reversed(range(n))) + names = list(reversed(df["name"].values)) + ce_p1 = list(reversed(df["ce_display_p1"].values)) + ce_p2 = list(reversed(df["ce_display_p2"].values)) + + quartile_idx = pd.cut(np.arange(n), bins=4, labels=False) + colors = [_QUARTILE_COLORS[int(q)] for q in quartile_idx] + + bars1 = ax1.barh(y_positions, ce_p1, color=colors, edgecolor="white") + ax1.set_yticks(y_positions) + ax1.set_yticklabels(names) + ax1.set_xlabel("Cost per 0.1 SD (USD)") + ax1.set_xscale("log") + ax1.set_title("Phase 1 — Unadjusted") + ax1.invert_yaxis() + for bar, value in zip(bars1, ce_p1): + ax1.text( + value * 1.02, + bar.get_y() + bar.get_height() / 2, + f"${value:.2f}", + va="center", + fontsize=8, + ) + + bars2 = ax2.barh(y_positions, ce_p2, color=colors, edgecolor="white") + ax2.set_xlabel("Cost per 0.1 SD (USD)") + ax2.set_xscale("log") + ax2.set_title("Phase 2 — Time-Adjusted") + ax2.invert_yaxis() + for bar, value in zip(bars2, ce_p2): + ax2.text( + value * 1.02, + bar.get_y() + bar.get_height() / 2, + f"${value:.2f}", + va="center", + fontsize=8, + ) + + legend_handles = [ + plt.Rectangle((0, 0), 1, 1, color=c, label=label) + for c, label in zip(_QUARTILE_COLORS, _QUARTILE_LABELS) + ] + ax1.legend(handles=legend_handles, loc="lower right", fontsize=7) + + fig.suptitle( + "League Table: Before vs. After Time Adjustment", fontsize=13, fontweight="bold" + ) + sns.despine() + fig.tight_layout() + return fig, (ax1, ax2) diff --git a/tests/test_time_horizon.py b/tests/test_time_horizon.py new file mode 100644 index 0000000..5b43ab7 --- /dev/null +++ b/tests/test_time_horizon.py @@ -0,0 +1,262 @@ +import pytest + +from cea_dev.core import Intervention, InterventionSet +from cea_dev.time_horizon import ( + DiscountedIntervention, + DiscountedInterventionSet, +) + + +def _make_single() -> Intervention: + return Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=1, + annual_decay_rate=1.0, + discount_rate=0.03, + ) + + +def _make_set() -> InterventionSet: + return InterventionSet( + [ + Intervention( + name="A", + effect_sd=0.20, + effect_se=0.05, + cost_est=4.0, + cost_low=3.2, + cost_high=4.8, + fixed_cost=0.0, + variable_cost_per_participant=4.0, + compliance_rate=1.0, + duration_years=3, + annual_decay_rate=0.90, + ), + Intervention( + name="B", + effect_sd=0.10, + effect_se=0.03, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + fixed_cost=0.0, + variable_cost_per_participant=2.0, + compliance_rate=1.0, + duration_years=1, + annual_decay_rate=1.0, + ), + ] + ) + + +class TestDiscountedIntervention: + def test_discounted_effect_recovers_baseline(self): + iv = _make_single() + di = DiscountedIntervention(iv) + assert di.discounted_effect == pytest.approx(iv.effect_sd) + + def test_discounted_effect_exceeds_effect_for_long_duration(self): + iv = _make_single() + long = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=5, + annual_decay_rate=0.95, + ) + di_long = DiscountedIntervention(long) + assert di_long.discounted_effect > iv.effect_sd + + def test_discounted_effect_equals_effect_when_decay_is_zero(self): + iv = _make_single() + full_decay = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=3, + annual_decay_rate=0.0, + ) + di = DiscountedIntervention(full_decay) + assert di.discounted_effect == pytest.approx(iv.effect_sd) + + def test_adjusted_cost_recovers_phase1_when_no_fixed_cost(self): + iv = _make_single() + di = DiscountedIntervention(iv) + assert di.adjusted_cost == pytest.approx(iv.cost_est) + + def test_adjusted_cost_higher_with_low_compliance(self): + iv = _make_single() + low_c = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=0.70, + ) + di = DiscountedIntervention(low_c) + assert di.adjusted_cost > iv.cost_est + + def test_adjusted_cost_includes_fixed_cost_per_beneficiary(self): + with_fixed = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=1000.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + ) + di = DiscountedIntervention(with_fixed, n_beneficiaries=1000) + assert di.adjusted_cost == pytest.approx(1.0 + 5.0) + + def test_effectiveness_accounts_for_time_and_cost(self): + iv = _make_single() + base = DiscountedIntervention(iv) + + double_duration = Intervention( + name="X", + effect_sd=0.10, + effect_se=0.02, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=2, + annual_decay_rate=1.0, + ) + di = DiscountedIntervention(double_duration) + assert di.effectiveness > base.effectiveness + + def test_ce_display_roundtrip(self): + iv = _make_single() + di = DiscountedIntervention(iv) + assert di.ce_display == pytest.approx(0.1 / di.effectiveness) + + +class TestDiscountedInterventionSet: + def test_league_table_sorted(self): + iset = _make_set() + diset = DiscountedInterventionSet.from_intervention_set(iset) + table = diset.league_table() + eff = table["effectiveness"].values + assert all(eff[i] >= eff[i + 1] for i in range(len(eff) - 1)) + + def test_league_table_has_columns(self): + iset = _make_set() + diset = DiscountedInterventionSet.from_intervention_set(iset) + table = diset.league_table() + assert set(table.columns) >= { + "name", + "discounted_effect", + "adjusted_cost", + "effectiveness", + "ce_display", + } + + def test_ranking_shift_has_correct_columns(self): + iset = _make_set() + diset = DiscountedInterventionSet.from_intervention_set(iset) + shift = diset.ranking_shift(iset) + assert set(shift.columns) >= { + "name", + "rank_phase1", + "rank_phase2", + "rank_shift", + } + + def test_ecd_improves_with_long_duration(self): + ecd = Intervention( + name="ECD", + effect_sd=0.22, + effect_se=0.08, + cost_est=25.0, + cost_low=20.0, + cost_high=30.0, + fixed_cost=0.0, + variable_cost_per_participant=25.0, + compliance_rate=1.0, + duration_years=10, + annual_decay_rate=0.90, + ) + cheap = Intervention( + name="Cheap Short", + effect_sd=0.05, + effect_se=0.02, + cost_est=2.0, + cost_low=1.6, + cost_high=2.4, + fixed_cost=0.0, + variable_cost_per_participant=2.0, + compliance_rate=1.0, + duration_years=1, + annual_decay_rate=1.0, + ) + iset = InterventionSet([ecd, cheap]) + diset = DiscountedInterventionSet.from_intervention_set(iset) + + table = diset.league_table() + assert table.iloc[0]["name"] == "ECD" + + def test_short_duration_interventions_fall(self): + long_d = Intervention( + name="Long", + effect_sd=0.10, + effect_se=0.03, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=5, + annual_decay_rate=0.90, + ) + short = Intervention( + name="Short", + effect_sd=0.15, + effect_se=0.03, + cost_est=5.0, + cost_low=4.0, + cost_high=6.0, + fixed_cost=0.0, + variable_cost_per_participant=5.0, + compliance_rate=1.0, + duration_years=1, + annual_decay_rate=1.0, + ) + iset = InterventionSet([short, long_d]) + diset = DiscountedInterventionSet.from_intervention_set(iset) + + p1_names = list(iset.league_table()["name"]) + assert p1_names[0] == "Short" + + p2_names = list(diset.league_table()["name"]) + assert p2_names[0] == "Long" From 5d42e54f334781eb571c70f7fcdf15944c82bbca Mon Sep 17 00:00:00 2001 From: Julius Ruschenpohler <45004123+ruschenpohler@users.noreply.github.com> Date: Wed, 3 Jun 2026 23:04:22 +0800 Subject: [PATCH 5/5] docs: fill README Section 4 with Phase 2 time-adjusted league table [4] --- README.md | 39 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c86e790..69cb526 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,44 @@ See `notebooks/01_core_model.ipynb` for the computational version and chart. ## 4. Adjustments: Time Horizon and Discounting [→ notebook 02] -*(To be implemented in Phase 2.)* +The Phase 1 league table assumed three things: all costs are proportional to beneficiaries, effects last exactly one year, and they do not decay. All three are wrong for most real interventions. This section relaxes them. + +### The adjustment gap + +Published evaluations of development interventions rarely report the data needed to adjust for external validity, counterfactual conditions, or implementation fidelity at scale. Of the adjustments that *can* be implemented from published data, time horizon and discounting are the most consequential — and the most defensible. Cost structure (fixed vs. variable) can also be adjusted when studies report cost components separately, though this is uncommon. (For a full discussion of what cannot be adjusted, see Section 2.) + +### Discounted effective effect + +The effective discounted effect of intervention $i$ is: + +$$\hat{\tau}_i^{eff} = \hat{\tau}_i \cdot \sum_{t=0}^{T_i - 1} \frac{\rho_i^t}{(1+r)^t}$$ + +where $T_i$ is the duration in years, $\rho_i \in (0,1]$ is the annual retention rate (fraction of the initial effect remaining each year), and $r$ is the annual discount rate (default 0.03). When $\rho_i = 1$ and $T_i = 1$, this reduces to $\hat{\tau}_i$, recovering the Phase 1 case. + +### Fixed vs. variable costs + +Relaxing Assumption A1: adjusted cost is defined as + +$$C_i^{adj} = \frac{C_i^{fixed}}{N} + \frac{C_i^{var}}{p_{c,i}}$$ + +where $N$ is the number of beneficiaries (held constant across interventions for comparability, default $N=1{,}000$) and $p_{c,i}$ is the compliance rate. When $C_i^{fixed} = 0$, this reduces to the proportional-cost case. + +### The time-adjusted league table + +| Rank | Intervention | Phase 1 CE* ($) | Phase 2 CE* ($) | Rank shift | +|---|---|---|---|---| +| 1 | Teaching at the Right Level (TaRL) | 2.22 | 1.48 | — | +| 2 | Early childhood development (ECD) | 11.36 | 2.29 | ↑3 | +| 3 | Deworming | 4.00 | 4.39 | ↓1 | +| 4 | Structured pedagogy | 6.67 | 4.44 | ↓1 | +| 5 | Computer-assisted learning (CAL) | 8.57 | 5.00 | ↓1 | +| 6 | Teacher performance pay | 18.00 | 13.49 | ↑1 | +| 7 | School meals | 16.67 | 17.85 | ↓1 | +| 8 | Conditional cash transfer (CCT) | 31.43 | 21.29 | — | + +**Headline result:** ECD moves from 5th to 2nd when its 10-year duration is accounted for. Short-duration interventions with low retention (school meals, deworming) lose ground. TaRL remains first, but the gap narrows. + +See `notebooks/02_time_horizon.ipynb` for the computational version and side-by-side chart. ---