arXiv 20 Apr 2024 · Econometrics · 3 citations (OpenAlex)
arXiv:2404.13356 · PDF · DOI · OpenAlex · Extracted main text
This methodological review examines the use of the causal forest method by applied researchers across 133 peer-reviewed papers. It shows that the emerging best practice relies heavily on the approach and tools created by the original authors of the causal forest such as their grf package and the approaches given by them in examples. Generally researchers use the causal forest on a relatively low-dimensional dataset relying on observed controls or in some cases experiments to identify effects. There are several common ways to then communicate results -- by mapping out the univariate distribution of individual-level treatment effect estimates, displaying variable importance results for the forest and graphing the distribution of treatment effects across covariates that are important either for theoretical reasons or because they have high variable importance. Some deviations from this common practice are interesting and deserve further development and use. Others are unnecessary or even harmful. The paper concludes by reflecting on the emerging best practice for causal forest use and paths for future research.
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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, Tibshirani \ Wager (2019) `Generalized random forests', The Annals of Statistics 47(2), 1148–1178 | 1.000 | 13 | 4 | 100% |
| 2 | Wager \ Athey (2018) `Estimation and Inference of Heterogeneous Treatment Effects using Random Forests', Journal of the American Statistical Associat… | 1.000 | 8 | 6 | 100% |
| 3 | Rehill \ Biddle (2024) `Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability', Data & Poli… | 1.000 | 5 | 4 | 100% |
| 4 | Cockx, Lechner \ Bollens (2023) `Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium', Labour Economics 80, 102306 | 1.000 | 5 | 3 | 100% |
| 5 | Guo, Sriram \ Manchanda (2021) `The Effect of Information Disclosure on Industry Payments to Physicians', Journal of Marketing Research 58(1), 115–140 | 1.000 | 5 | 3 | 100% |
| 6 | Osawa, Goto, Kudo, Hayakawa, Yamakawa, Kushimoto, Foster, Kellum \ Doi (2023) `Targeted therapy using polymyxin B hemadsorption in patients with sepsis: a post-hoc analysis of the JSEPTIC-DIC study and the… | 1.000 | 5 | 3 | 100% |
| 7 | Yadlowsky, Fleming, Shah, Brunskill \ Wager (2023) `Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects' | 0.928 | 5 | 3 | 80% |
| 8 | Athey \ Imbens (2016) `Recursive partitioning for heterogeneous causal effects', Proceedings of the National Academy of Sciences 113(27), 7353–7360 | 0.928 | 4 | 3 | 100% |
| 9 | Breiman (2001) `Random Forests', Machine Learning 45(1), 5–32 | 0.928 | 4 | 3 | 100% |
| 10 | Tibshirani, Athey, Sverdrup \ Wager (2021) `Package 'grf” | 0.928 | 4 | 3 | 100% |
Showing the top 10 of 108 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distilling interpretable causal trees from causal forests | 1.000 | 5 | 3 |