arXiv 2 Aug 2024 · Econometrics
arXiv:2408.01023 · PDF · DOI · OpenAlex · Extracted main text
Machine learning methods for estimating treatment effect heterogeneity promise greater flexibility than existing methods that test a few pre-specified hypotheses. However, one problem these methods can have is that it can be challenging to extract insights from complicated machine learning models. A high-dimensional distribution of conditional average treatment effects may give accurate, individual-level estimates, but it can be hard to understand the underlying patterns; hard to know what the implications of the analysis are. This paper proposes the Distilled Causal Tree, a method for distilling a single, interpretable causal tree from a causal forest. This compares well to existing methods of extracting a single tree, particularly in noisy data or high-dimensional data where there are many correlated features. Here it even outperforms the base causal forest in most simulations. Its estimates are doubly robust and asymptotically normal just as those of the causal forest are.
appendix boundary found by appendix_titled_section at “Appendix --- Detailed simulation results” · 97% of the source is main text. Read the extracted text to check this.
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 | Rudin (2019) `Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead', Nature Machi… | 1.000 | 6 | 4 | 100% |
| 2 | Athey, Tibshirani \ Wager (2019) `Generalized random forests', The Annals of Statistics 47(2), 1148–1178 | 1.000 | 5 | 4 | 100% |
| 3 | Rehill (2024) `How do applied researchers use the causal forest? a methodological review of a method' self | 1.000 | 5 | 3 | 100% |
| 4 | Athey \ Imbens (2016) `Recursive partitioning for heterogeneous causal effects', Proceedings of the National Academy of Sciences 113(27), 7353–7360 | 0.928 | 4 | 4 | 100% |
| 5 | Breiman (2001) `Random Forests', Machine Learning 45(1), 5–32 | 0.928 | 4 | 4 | 100% |
| 6 | Künzel, Sekhon, Bickel \ Yu (2019) `Metalearners for estimating heterogeneous treatment effects using machine learning', Proceedings of the National Academy of Sci… | 0.928 | 4 | 3 | 100% |
| 7 | Shiba \ Inoue (2024) `Harnessing causal forests for epidemiologic research: key considerations', American Journal of Epidemiology 193(6), 813–818 | 0.928 | 4 | 3 | 100% |
| 8 | Wager \ Athey (2018) `Estimation and Inference of Heterogeneous Treatment Effects using Random Forests', Journal of the American Statistical Associat… | 0.928 | 4 | 3 | 100% |
| 9 | Dao, Kamath, Syrgkanis \ Mackey (2021) Knowledge Distillation as Semiparametric Inference, in `International Conference on Learning Representations' | 0.874 | 5 | 2 | 100% |
| 10 | Frosst \ Hinton (2017) `Distilling a Neural Network Into a Soft Decision Tree' | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | How do applied researchers use the Causal Forest? A methodological review | 0.737 | 3 | 2 |