Timezone: »

Optimization-based Causal Estimation from Heterogenous Environments
Mingzhang Yin · Yixin Wang · David Blei
Event URL: https://openreview.net/forum?id=W0eb_XYb53h »

This paper presents an optimization approach to causal estimation. In classical machine learning, the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but prevent us from interpreting the result causally. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments. Given datasets from multiple environments---and ones that exhibit enough heterogeneity---CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and the recently-proposed IRMv1, CoCo provides more accurate estimates of the causal model.

Author Information

Mingzhang Yin (Columbia University)
Yixin Wang (Columbia University)
David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

More from the Same Authors