arXiv 16 Dec 2018 · Econometrics · publishedLabour Economics (2023) · 6 citations (OpenAlex)
arXiv:1812.06533 · PDF · DOI · OpenAlex · Extracted main text
Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.
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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 | Bitler, Gelbach, and Hoynes (2006) What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments | 1.000 | 5 | 5 | 100% |
| 2 | Bitler, Gelbach, and Hoynes (2017) Can Variation in Subgroups' Average Treatment Effects Explain Treatment Effect Heterogeneity? Evidence from a Social Experiment | 1.000 | 5 | 4 | 100% |
| 3 | Kline and Tartari (2016) Bounding the Labor Supply Responses to a Randomized Welfare Experiment: A Revealed Preference Approach | 1.000 | 5 | 3 | 100% |
| 4 | Athey, Tibshirani, and Wager (2018) Generalized Random Forests | 0.737 | 3 | 2 | 100% |
| 5 | Athey and Imbens (2016) Recursive Partitioning for Heterogeneous Causal Effects | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters | 0.644 | 2 | 2 | 100% |
| 7 | Hastie, Tibshirani, and Friedman (2009) Elemants of Statistical Learning: Data Mining, Inference, and Prediction | 0.644 | 2 | 2 | 100% |
| 8 | Athey and Wager (2018) Efficient Policy Learning | 0.405 | 1 | 1 | 100% |
| 9 | Lee, Okui, and Whang (2017) Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function | 0.405 | 1 | 1 | 100% |
| 10 | Wager and Athey (2018) Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests | 0.405 | 1 | 1 | 100% |
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