Pedro H. C. Sant'Anna, Xiaojun Song
arXiv 18 Nov 2016 · Statistics — Methodology · publishedJournal of Econometrics (2019) · 33 citations (OpenAlex)
arXiv:1611.06217 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes new nonparametric diagnostic tools to assess the asymptotic validity of different treatment effects estimators that rely on the correct specification of the propensity score. We derive a particular restriction relating the propensity score distribution of treated and control groups, and develop specification tests based upon it. The resulting tests do not suffer from the "curse of dimensionality" when the vector of covariates is high-dimensional, are fully data-driven, do not require tuning parameters such as bandwidths, and are able to detect a broad class of local alternatives converging to the null at the parametric rate $n^{-1/2}$, with $n$ the sample size. We show that the use of an orthogonal projection on the tangent space of nuisance parameters facilitates the simulation of critical values by means of a multiplier bootstrap procedure, and can lead to power gains. The finite sample performance of the tests is examined by means of a Monte Carlo experiment and an empirical application. Open-source software is available for implementing the proposed tests.
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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 | Shaikh, Simonsen, Vytlacil \ Yildiz (2009) A specification test for the propensity score using its distribution conditional on participation | 0.968 | 32 | 6 | 91% |
| 2 | Millimet \ Tchernis (2009) On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies | 0.874 | 10 | 2 | 100% |
| 3 | Escanciano \ Goh (2014) Specification analysis of linear quantile models | 0.811 | 4 | 2 | 100% |
| 4 | Escanciano (2009) Simple bootstrap tests for conditional moment restrictions,, Technical report, Indiana University | 0.737 | 3 | 2 | 100% |
| 5 | Heckman, Ichimura, Smith \ Todd (1998) Characterizing selection bias using experimental data | 0.737 | 3 | 2 | 100% |
| 6 | Rosenbaum \ Rubin (1983) The central role of the propensity score in observational studies for causal effects | 0.737 | 3 | 2 | 100% |
| 7 | van der Vaart \ Wellner (1996) Weak Convergence and Empirical Processes, New York: Springer | 0.644 | 4 | 2 | 50% |
| 8 | Frankel \ Rose (2005) Is Trade Good or Bad for the Environment? Sorting Out the Causality | 0.644 | 4 | 1 | 100% |
| 9 | Abadie \ Imbens (2016) Matching on the estimated propensity score | 0.644 | 2 | 2 | 100% |
| 10 | Bierens \ Ploberger (1997) Asymptotic theory of integrated conditional moment tests | 0.644 | 2 | 2 | 100% |
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