A fundamental causal modelling task is to predict the effect of an intervention (or treatment) on an outcome, given context/covariates. Examples include predicting the effect of a medical treatment on patient health given patient symptoms and demographic information, or predicting the effect of ticket pricing on airline sales given seasonal fluctuations in demand. The problem becomes especially challenging when the treatment and context are complex (for instance, "treatment" might be a web ad design or a radiotherapy plan), and when only observational data is available (i.e., we have access to historical data, but cannot intervene ourselves). The challenge is greater still when the covariates are not observed, and constitute a hidden source of confounding.

We will provide practical tools and methods for estimating causal effects of complex, high dimensional treatments from observational data. The approach is based on conditional feature means, which represent conditional expectations of relevant model features. These features can be deep neural nets (adaptive, finite dimensional, learned from data), or kernel features (fixed, infinite dimensional, enforcing smoothness). When hidden confounding is present, we will demonstrate a neural net implementation of instrumental variable regression to correct for this confounding. We will apply these methods to modelling employment outcomes …

Practitioners will often analyze a data sample with the goal of applying any conclusions to a new population. For instance, if economists conclude microcredit is effective at alleviating poverty based on observed data, policymakers might decide to distribute microcredit in other locations or future years. Typically, the original data is not a perfect random sample from the population where policy is applied -- but researchers might feel comfortable generalizing anyway so long as deviations from random sampling are small, and the corresponding impact on conclusions is small as well. Conversely, researchers might worry if a very small proportion of the data sample was instrumental to the original conclusion. So we propose a method to assess the sensitivity of statistical conclusions to the removal of a very small fraction of the data set. Manually checking all small data subsets is computationally infeasible, so we propose an approximation based on the classical influence function. Our method is automatically computable for common estimators. We provide finite-sample error bounds on approximation performance and a low-cost exact lower bound on sensitivity. We find that sensitivity is driven by a signal-to-noise ratio in the inference problem, does not disappear asymptotically, and is not decided by misspecification. …

Evaluation in Machine Learning does not always get the attention it deserves. I hope to focus our attention for the time of this talk on the questions of systematic evaluation in machine learning and the changes that we should continue to make as we elevate the standard of evaluation across our field. The breadth of application areas we collaborate on in machine learning requires a variety of approaches for evaluation, and we’ll explore this variety by considering applications in generative models, social good, healthcare, and environmental science. Grounded in these applications, we will expand the conceptual aperture through which we think about machine learning evaluations, starting from purely technical evaluations (thinking about likelihoods), moving to mixed methods (with proper scoring rules and expert assessments), and then to sociotechnical assessments (considering fairness, impacts, and participation). My core message is that broad and expansive evaluation remains fundamental and an area into which I hope we will drive even greater investments as a community, together.