Jianqing Fan, Frederick L. Moore ’18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering, Princeton University
Details
Friday, September 13, 2024 (10:30 AM – 11:30 AM ET)
Location: Columbia School of Social Work, Room 903
Neural Causal Pursuit: Adversarial Invariance Learning from Heterogeneous Environments
Abstract: This talk develops nonparametric invariance and causal learning from multiple environments regression models in which data from heterogeneous experimental settings are collected. The joint distribution of the response variable and covariate may vary across different environments. Yet, the conditional expectation of outcome given the unknown set of important or quasi-causal variables is invariant across environments. Our idea of invariance and causal learning is to find a set of variables as exogenous as possible across multiple environments to minimize the empirical loss. To realize this idea, we proposed a Neural Adversial Invariant Learning (NAIL) framework, in which the unknown regression is represented by a Relu network, and invariance across multiple environments is tested using adversarial neural networks. Leveraging the representation power of neural networks, we introduce neural causal networks based on a focus adversarial invariance regularization (FAIR) and its novel training algorithm. It is shown that FAIR-NN can find the invariant variables and quasi-causal variables and that the resulting procedure is adaptive to low-dimensional composition structures. The combinatorial optimization problem is implemented by a Gumble approximation with decreased temperature and stochastic approximations. The procedures are convincingly demonstrated using simulated examples. (Joint work with Cong Fang, Yihong Gu, and Peter Buelhmann)
