Summary of C-learner: Constrained Learning For Causal Inference and Semiparametric Statistics, by Tiffany Tianhui Cai et al.
C-Learner: Constrained Learning for Causal Inference and Semiparametric Statistics
by Tiffany Tianhui Cai, Yuri Fonseca, Kaiwen Hou, Hongseok Namkoong
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel debiased estimator achieves a balance between statistical efficiency and stability by solving for the best plug-in estimator under the constraint that the first-order error with respect to the plugged-in quantity is zero. This framework leverages flexible model classes including neural networks and tree ensembles, allowing it to perform well in challenging settings with limited overlap between treatment and control groups. The constrained learning approach outperforms existing methods such as one-step estimation and targeted maximum likelihood estimation in several experimental settings, even when handling text-based covariates by fine-tuning language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to estimate the average effect of a treatment is proposed. This method combines the benefits of two previous approaches: it’s stable like simple plug-in estimators but has good properties like debiased causal estimation methods. The new method uses flexible models and does well in situations where there isn’t much overlap between people who got the treatment and those who didn’t. |
Keywords
» Artificial intelligence » Fine tuning » Likelihood
