arXiv 9 Sep 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2109.04154 · PDF · DOI · OpenAlex · Extracted main text
Estimating a causal effect from observational data can be biased if we do not control for self-selection. This selection is based on confounding variables that affect the treatment assignment and the outcome. Propensity score methods aim to correct for confounding. However, not all covariates are confounders. We propose the outcome-adaptive random forest (OARF) that only includes desirable variables for estimating the propensity score to decrease bias and variance. Our approach works in high-dimensional datasets and if the outcome and propensity score model are non-linear and potentially complicated. The OARF excludes covariates that are not associated with the outcome, even in the presence of a large number of spurious variables. Simulation results suggest that the OARF produces unbiased estimates, has a smaller variance and is superior in variable selection compared to other approaches. The results from two empirical examples, the effect of right heart catheterization on mortality and the effect of maternal smoking during pregnancy on birth weight, show comparable treatment effects to previous findings but tighter confidence intervals and more plausible selected variables.
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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 | Susan M Shortreed and Ashkan Ertefaie (2017) Outcome-adaptive lasso: Variable selection for causal inference | 0.950 | 7 | 4 | 86% |
| 2 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 3 | 67% |
| 3 | Keisuke Hirano and Guido W Imbens (2001) Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization | 0.644 | 2 | 2 | 100% |
| 4 | Douglas Almond, Kenneth Y Chay, and David S Lee (2005) The costs of low birth weight | 0.511 | 2 | 1 | 100% |
| 5 | Alfred F Connors, Theodore Speroff, Neal V Dawson, Charles Thomas, F… (1996) The effectiveness of right heart catheterization in the initial care of critically iii patients | 0.511 | 2 | 1 | 100% |
| 6 | Houtao Deng and George Runger (2013) Gene selection with guided regularized random forest | 0.511 | 2 | 1 | 100% |
| 7 | Stefano Nembrini, Inke R König, and Marvin N Wright (2018) The revival of the gini importance? | 0.511 | 2 | 1 | 100% |
| 8 | Daniel Westreich, Justin Lessler, and Michele Jonsson Funk (2010) Propensity score estimation: neural networks, support vector machines, decision trees (cart), and meta-classifiers as alternativ… | 0.511 | 2 | 1 | 100% |
| 9 | Richard K Crump, V Joseph Hotz, Guido W Imbens, and Oscar A Mitnik (2009) Dealing with limited overlap in estimation of average treatment effects | 0.405 | 1 | 1 | 100% |
| 10 | Houtao Deng and George Runger (2012) Feature selection via regularized trees | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.