Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
arXiv 31 Dec 2022 · Econometrics
arXiv:2301.00251 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available with conventional methods. In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characteristics. To achieve this, we employ partial least squares to identify target components of the population and causal forests to estimate personalized policy effects across these components. We show that the method is consistent and leads to asymptotically normally distributed policy effects. To demonstrate the efficacy of our approach, we apply it to the data from the Pennsylvania Reemployment Bonus Experiments, which were conducted in 1988-1989. The analysis reveals that financial incentives can motivate some young non-white individuals to enter the labor market. However, these incentives may also provide a temporary financial cushion for others, dissuading them from actively seeking employment. Our findings highlight the need for targeted, personalized measures for young non-white male participants.
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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 | Stefan Wager and Susan Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.865 | 17 | 5 | 65% |
| 2 | Maria Nareklishvili (2022) Adaptive estimation of partially identified treatment effects self | 0.843 | 5 | 3 | 60% |
| 3 | Nicolai Meinshausen and Greg Ridgeway (2006) Quantile regression forests | 0.843 | 3 | 3 | 100% |
| 4 | Susan Athey and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.811 | 4 | 2 | 100% |
| 5 | Victor Chernozhukov, Mert Demirer, Esther Duflo, and Ivan Fernandez-… (2018) Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immuniza… | 0.644 | 4 | 1 | 100% |
| 6 | David R Brillinger (2012) A generalized linear model with “gaussian” regressor variables | 0.511 | 2 | 2 | 50% |
| 7 | Kevin Li (2020) Asymptotic normality for multivariate random forest estimators | 0.511 | 2 | 2 | 50% |
| 8 | Stefan Wager and Guenther Walther (2015) Adaptive concentration of regression trees, with application to random forests | 0.511 | 2 | 2 | 50% |
| 9 | P Richard Hahn, Jared S Murray, and Carlos M Carvalho (2020) Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion) | 0.511 | 2 | 1 | 100% |
| 10 | Inge S Helland (1990) Partial least squares regression and statistical models | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 60 scored citations.