arXiv 14 May 2018 · Econometrics · 1 citations (OpenAlex)
arXiv:1805.05067 · PDF · DOI · OpenAlex · Extracted main text
This paper contributes to the literature on treatment effects estimation with machine learning inspired methods by studying the performance of different estimators based on the Lasso. Building on recent work in the field of high-dimensional statistics, we use the semiparametric efficient score estimation structure to compare different estimators. Alternative weighting schemes are considered and their suitability for the incorporation of machine learning estimators is assessed using theoretical arguments and various Monte Carlo experiments. Additionally we propose an own estimator based on doubly robust Kernel matching that is argued to be more robust to nuisance parameter misspecification. In the simulation study we verify theory based intuition and find good finite sample properties of alternative weighting scheme estimators like the one we propose.
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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 | Athey, Susan, Imbens, Guido W., Wager, Stefan (2018) Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions | 0.956 | 8 | 4 | 88% |
| 2 | Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian (2014) Inference on Treatment Effects after Selection among High-Dimensional Controls | 0.928 | 10 | 5 | 80% |
| 3 | Busso, Matias, DiNardo, John, McCrary, Justin (2014) New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators | 0.843 | 3 | 3 | 100% |
| 4 | Tsiatis, Anastasios A (2006) Semiparametric Theory and Missing Data | 0.822 | 6 | 2 | 83% |
| 5 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2017) Double/Debiased Machine Learning for Treatment and Structural Parameters | 0.811 | 4 | 2 | 100% |
| 6 | Abadie, Alberto, Imbens, Guido W (2006) Large Sample Properties of Matching Estimators for Average Treatment Effects | 0.737 | 3 | 2 | 100% |
| 7 | Heckman, James J., Ichimura, Hidehiko, Todd, Petra (1998) Matching As An Econometric Evaluation Estimator | 0.737 | 3 | 2 | 100% |
| 8 | Kennedy, Edward H (2016) Semiparametric Theory and Empirical Processes in Causal Inference | 0.737 | 3 | 2 | 100% |
| 9 | Lechner, Michael, Miquel, Ruth, Wunsch, Conny (2011) Long-Run Effects of Public Sector Sponsored Training in West Germany | 0.737 | 3 | 2 | 100% |
| 10 | Zubizarreta, José R (2015) Stable Weights that Balance Covariates for Estimation with Incomplete Outcome Data | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.