Jul 12, 2020
Latent confounders—unobserved variables that influence both treatment and outcome—can bias estimates of causal effects. In some cases, these confounders are shared across observations, e.g. all students in a school are influenced by the school's culture in addition to any educational interventions they receive individually. This paper shows how to model latent confounders that have this structure and thereby improve estimates of causal effects. The key innovations are a hierarchical Bayesian model, Gaussian processes with structured latent confounders (GP-SLC), and a Monte Carlo inference algorithm for this model based on elliptical slice sampling. GP-SLC provides principled Bayesian uncertainty estimates of individual treatment effect without requiring parametric assumptions about the functional forms relating confounders, covariates, treatment, and outcomes. This paper also proves that, for linear functional forms, accounting for the structure in latent confounders is sufficient for asymptotically consistent estimates of causal effect. Finally, this paper shows GP-SLC is competitive with or more accurate than widely used causal inference techniques such as multi-level linear models and Bayesian additive regression trees. Benchmark datasets include the Infant Health and Development Program and a dataset showing the effect of changing temperatures on state-wide energy consumption across New England.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Presentations on similar topic, category or speaker