Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, Jacob Gates Foster
arXiv 9 Oct 2021 · Machine Learning · publishedSociological Methods & Research (2024) · 7 citations (OpenAlex)
arXiv:2110.04442 · PDF · DOI · OpenAlex · Extracted main text
This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.
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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 | Van der Laan, Mark J and Sherri Rose (2011) Targeted Learning: Causal Inference for Observational and Experimental Data\/ | 1.000 | 9 | 4 | 100% |
| 2 | Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 8 | 5 | 100% |
| 3 | Shi, Claudia, David Blei, and Victor Veitch (2019) Adapting Neural Networks for the Estimation of Treatment Effects | 1.000 | 5 | 4 | 100% |
| 4 | Wager, Stefan and Susan Athey (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.928 | 4 | 3 | 100% |
| 5 | Hill, Jennifer L (2011) Bayesian Nonparametric Modeling for Causal Inference | 0.843 | 4 | 3 | 75% |
| 6 | Johansson, Fredrik D, Nathan Kallus, Uri Shalit, and David Sontag (2018) Learning Weighted Representations for Generalization Across Designs | 0.843 | 5 | 4 | 60% |
| 7 | Chernozhukov, Victor, Whitney Newey, Victor M Quintas-Martinez, and… (2022) Riesznet and forestriesz: Automatic debiased machine learning with neural nets and random forests | 0.843 | 3 | 3 | 100% |
| 8 | Johansson, Fredrik D., Uri Shalit, Nathan Kallus, and David A. Sontag (2020) Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects | 0.822 | 9 | 6 | 56% |
| 9 | Kennedy, Edward H (2016) Semiparametric Theory and Empirical Processes in Causal Inference | 0.811 | 4 | 2 | 100% |
| 10 | Zivich, Paul N and Alexander Breskin (2021) Machine learning for causal inference: on the use of cross-fit estimators | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 107 scored citations.