arXiv 23 Nov 2020 · Econometrics · publishedJournal of Causal Inference (2024) · 3 citations (OpenAlex)
arXiv:2011.11485 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a new class of M-estimators that double weight for the twin problems of nonrandom treatment assignment and missing outcomes, both of which are common issues in the treatment effects literature. The proposed class is characterized by a `robustness' property, which makes it resilient to parametric misspecification in either a conditional model of interest (for example, mean or quantile function) or the two weighting functions. As leading applications, the paper discusses estimation of two specific causal parameters; average and quantile treatment effects (ATE, QTEs), which can be expressed as functions of the doubly weighted estimator, under misspecification of the framework's parametric components. With respect to the ATE, this paper shows that the proposed estimator is doubly robust even in the presence of missing outcomes. Finally, to demonstrate the estimator's viability in empirical settings, it is applied to Calonico and Smith (2017)'s reconstructed sample from the National Supported Work training program.
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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 | Calónico, S. and J. Smith (2017) The women of the national supported work demonstration | 0.874 | 6 | 5 | 67% |
| 2 | Angrist, J., V. Chernozhukov, and I. Fernández-Val (2006) Quantile Regression under Misspecification, with an Application to the U.S | 0.843 | 3 | 3 | 100% |
| 3 | White, H (1982) Maximum likelihood estimation of misspecified models | 0.843 | 3 | 3 | 100% |
| 4 | Wooldridge, J. M (2007) Inverse probability weighted estimation for general missing data problems | 0.843 | 3 | 3 | 100% |
| 5 | LaLonde, R. J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.737 | 3 | 3 | 67% |
| 6 | Koenker, R. and G. Bassett (1978) Regression Quantiles | 0.737 | 3 | 2 | 100% |
| 7 | Komunjer, I (2005) Quasi-maximum likelihood estimation for conditional quantiles | 0.737 | 3 | 2 | 100% |
| 8 | Firpo, S (2007) Efficient semiparametric estimation of quantile treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Soczyński, T. and J. M. Wooldridge (2018) A general double robustness result for estimating average treatment effects | 0.737 | 3 | 2 | 100% |
| 10 | Hirano, K. and G. W. Imbens (2001) Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2012.00745 | 0.511 | 2 | 1 |
| 2 | Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection | 0.405 | 1 | 1 |
| 3 | 2406.13826 | 0.405 | 1 | 1 |