Alexandre Poirier, Tymon Słoczyński
arXiv 22 Apr 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2404.14603 · PDF · DOI · OpenAlex · Extracted main text
In this paper we study a class of weighted estimands, which we define as parameters that can be expressed as weighted averages of the underlying heterogeneous treatment effects. The popular ordinary least squares (OLS), two-stage least squares (2SLS), and two-way fixed effects (TWFE) estimands are all special cases within our framework. Our focus is on answering two questions concerning weighted estimands. First, under what conditions can they be interpreted as the average treatment effect for some (possibly latent) subpopulation? Second, when these conditions are satisfied, what is the upper bound on the size of that subpopulation, either in absolute terms or relative to a target population of interest? We argue that this upper bound provides a valuable diagnostic for empirical research. When a given weighted estimand corresponds to the average treatment effect for a small subset of the population of interest, we say its internal validity is low. Our paper develops practical tools to quantify the internal validity of weighted estimands. We also apply these tools to revisit a prominent study of the effects of unilateral divorce laws on female suicide.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | de Chaisemartin and D'Haultfuille (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects | 1.000 | 5 | 3 | 100% |
| 2 | Goodman-Bacon (2021) Difference-in-Differences with Variation in Treatment Timing | 0.928 | 10 | 4 | 80% |
| 3 | Blandhol, Bonney, Mogstad, and Torgovitsky (2022) When Is TSLS Actually LATE? | 0.909 | 8 | 5 | 75% |
| 4 | Angrist (1998) Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants | 0.874 | 5 | 2 | 100% |
| 5 | Callaway and Sant'Anna (2021) Difference-in-Differences with Multiple Time Periods | 0.874 | 5 | 2 | 100% |
| 6 | Angrist and Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity | 0.811 | 4 | 2 | 100% |
| 7 | Soczyński (2020) When Should We (Not) Interpret Linear IV Estimands as LATE? | 0.811 | 4 | 2 | 100% |
| 8 | Stevenson and Wolfers (2006) Bargaining in the Shadow of the Law: Divorce Laws and Family Distress | 0.737 | 3 | 2 | 100% |
| 9 | Soczyński (2022) Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights | 0.737 | 3 | 2 | 100% |
| 10 | Wooldridge (2025) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators | 0.644 | 4 | 1 | 100% |
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