arXiv 19 Mar 2018 · Econometrics · 17 citations (OpenAlex)
arXiv:1803.07164 · PDF · DOI · OpenAlex · Extracted main text
We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of estimating the underling model as a zero-sum game between a modeler and an adversary and apply adversarial training. Our approach is similar in nature to Generative Adversarial Networks (GAN), though here the modeler is learning a representation of a function that satisfies a continuum of moment conditions and the adversary is identifying violating moments. We outline ways of constructing effective adversaries in practice, including kernels centered by k-means clustering, and random forests. We examine the practical performance of our approach in the setting of non-parametric instrumental variable regression.
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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 | Arjovsky, Martin, Chintala, Soumith, and Bottou, Léon (2017) Wasserstein gan | 0.737 | 3 | 2 | 100% |
| 2 | Rakhlin, Alexander and Sridharan, Karthik (2013) Optimization, learning, and games with predictable sequences | 0.644 | 2 | 2 | 100% |
| 3 | Shalev-Shwartz, Shai Online learning and online convex optimization | 0.644 | 2 | 2 | 100% |
| 4 | Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-… (2014) Generative adversarial nets | 0.511 | 2 | 1 | 100% |
| 5 | Kocaoglu, M., Snyder, C., Dimakis, A. G., and Vishwanath, S (2017) CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training | 0.511 | 2 | 1 | 100% |
| 6 | Anthony, Martin and Bartlett, Peter L (2009) Neural Network Learning: Theoretical Foundations | 0.405 | 1 | 1 | 100% |
| 7 | Athey, Susan, Tibshirani, Julie, and Wager, Stefan (2016) Generalized random forests | 0.405 | 1 | 1 | 100% |
| 8 | Bartlett, Peter L. and Mendelson, Shahar Rademacher and gaussian complexities: Risk bounds and structural results | 0.405 | 1 | 1 | 100% |
| 9 | Chamberlain, Gary (1987) Asymptotic efficiency in estimation with conditional moment restrictions | 0.405 | 1 | 1 | 100% |
| 10 | Chen, Xiaohong and Liao, Zhipeng (2015) Sieve semiparametric two-step gmm under weak dependence | 0.405 | 1 | 1 | 100% |
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