Bryan S. Graham, Cristine Campos de Xavier Pinto
arXiv 30 Oct 2018 · Econometrics · publishedJournal of Econometrics (2021) · 16 citations (OpenAlex)
arXiv:1810.12511 · PDF · DOI · OpenAlex · Extracted main text
Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b0(w) be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average beta0 = E[b0(W)]. When X is binary-valued (multi-valued) our procedure recovers the (a vector of) average treatment effect(s). When X is continuously-valued, or consists of multiple non-exclusive treatments, our estimand coincides with the average partial effect (APE) of X on Y when the underlying potential response function is linear in X, but otherwise heterogenous across agents. When the potential response function takes a general nonlinear/heterogenous form, and X is continuously-valued, our procedure recovers a weighted average of the gradient of this response across individuals and values of X. We provide a simple, and semiparametrically efficient, method of covariate adjustment for settings with complicated treatment regimes. Our method generalizes familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the partially linear regression model).
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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 | Wooldridge, J. M (2004) Estimating average partial effects under conditional moment independence assumptions | 1.000 | 21 | 5 | 100% |
| 2 | Cattaneo, M. D (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability | 1.000 | 8 | 5 | 100% |
| 3 | Wooldridge, J. M (2010) Econometric Analysis of Cross Section and Panel Data | 1.000 | 7 | 4 | 100% |
| 4 | Imbens, G. W (2000) The role of the propensity score in estimating dose-response functio | 1.000 | 5 | 4 | 100% |
| 5 | Wooldridge, J. M (1999) Distribution-free estimation of some nonlinear panel data models | 0.928 | 5 | 3 | 80% |
| 6 | Hahn, J (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.928 | 4 | 4 | 100% |
| 7 | Hirano, K., Imbens, G. W., & Ridder, G (2003) Efficient estimation of average treatment effects using the estimated propensity score | 0.928 | 4 | 3 | 100% |
| 8 | Angrist, J. D. & Krueger, A. B (1999) Handbook of Labor Economics, volume 3, chapter Empirical strategies in labor economics, (pp.\ 1277 – 1366.) | 0.874 | 6 | 2 | 100% |
| 9 | Robins, J. M., Mark, S. D., & Newey, W. K (1992) Estimating exposure effects by modelling the expectation of exposure conditional on confounders | 0.843 | 3 | 3 | 100% |
| 10 | Newey, W. K (1990) Semiparametric efficiency bounds | 0.822 | 9 | 6 | 56% |
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