Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu, Shumin Ma, Zhiri Yuan, Dongdong Wang, Zhixiang Huang
arXiv 5 Sep 2022 · Econometrics · 4 citations (OpenAlex)
arXiv:2209.01805 · PDF · DOI · OpenAlex · Extracted main text
Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE in the observational study. However, the DML estimators can suffer an error-compounding issue and even give an extreme estimate when the propensity scores are misspecified or very close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing literature solves this problem from a theoretical standpoint. In this paper, we propose a Robust Causal Learning (RCL) method to offset the deficiencies of the DML estimators. Theoretically, the RCL estimators i) are as consistent and doubly robust as the DML estimators, and ii) can get rid of the error-compounding issue. Empirically, the comprehensive experiments show that i) the RCL estimators give more stable estimations of the causal parameters than the DML estimators, and ii) the RCL estimators outperform the traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets.
appendix boundary found by none_found · 100% 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 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 12 | 4 | 100% |
| 2 | L. Mackey, V. Syrgkanis, and I. Zadik, “Orthogonal machine learning:… (2018) Orthogonal machine learning: Power and limitations | 1.000 | 8 | 3 | 100% |
| 3 | J. Robins, L. Li, E. Tchetgen, A. van der Vaart et al., “Higher orde… (2008) Higher order influence functions and minimax estimation of nonlinear functionals | 0.644 | 2 | 2 | 100% |
| 4 | U. Shalit, F. D. Johansson, and D. Sontag, “Estimating individual tr… (2017) Estimating individual treatment effect: generalization bounds and algorithms | 0.585 | 3 | 1 | 100% |
| 5 | C. Shi, D. Blei, and V. Veitch, “Adapting neural networks for the es… (2019) Adapting neural networks for the estimation of treatment effects | 0.585 | 3 | 1 | 100% |
| 6 | J. L. Hill, “Bayesian nonparametric modeling for causal inference,”… (2011) Bayesian nonparametric modeling for causal inference | 0.511 | 2 | 1 | 100% |
| 7 | J. Yoon, J. Jordon, and M. Van Der Schaar, “Ganite: Estimation of in… (2018) Ganite: Estimation of individualized treatment effects using generative adversarial nets | 0.511 | 2 | 1 | 100% |
| 8 | L. Yao, Z. Chu, S. Li, Y. Li, J. Gao, and A. Zhang, “A survey on cau… (2021) A survey on causal inference | 0.405 | 1 | 1 | 100% |
| 9 | A. M. Alaa and M. van der Schaar, “Bayesian inference of individuali… (2017) Bayesian inference of individualized treatment effects using multi-task gaussian processes | 0.405 | 1 | 1 | 100% |
| 10 | P. C. Austin and E. A. Stuart, “Moving towards best practice when us… (2015) Moving towards best practice when using inverse probability of treatment weighting (iptw) using the propensity score to estimate… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 29 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators | 0.405 | 1 | 1 |