MIT EECS, IMES, CSAIL
AI for health needs causality
Abstract: Recent success stories of using machine learning for diagnosing skin cancer, diabetic retinopathy, pneumonia, and breast cancer may give the impression that artificial intelligence (AI) is on the cusp of radically changing all aspects of health care. However, many of the most important problems, such as predicting disease progression, personalizing treatment to the individual, drug discovery, and finding optimal treatment policies, all require a fundamentally different way of thinking. Specifically, these problems require a focus on *causality* rather than simply prediction. Motivated by these challenges, my lab has been developing several new approaches for causal inference from observational data. In this talk, I describe our recent work on the deep Markov model (Krishnan, Shalit, Sontag AAAI '17) and TARNet (Shalit, Johansson, Sontag, ICML '17).
Primer on causal inference
Abstract: A common goal in science is to use knowledge gained by observing a phenomenon of interest to guide decision and policy making. If smokers are observed to have higher rates of lung cancer, should we legislate to discourage smoking? Such a policy will only be effective if smoking itself is the cause of cancer and the correlation between cancer rate and smoking is not explained by other factors, such as lifestyle choices. Problems like these are well described in the language of causal inference. In this primer, we explain the difference between statistical and causal reasoning, and introduce the notions of confounding, causal graphs and counterfactuals. We cover the problem of estimating causal effects from experimental and observational data, as well as sufficient assumptions to make causal statements based on statistical quantities.