Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress In Predictive Inference
Emmanuel Candès – Stanford University
Recent progress in machine learning provides us with many potentially effective tools to learn from datasets of ever increasing sizes and make useful predictions. How do we know that these tools can be trusted in critical and high-sensitivity systems? This talk introduces statistical ideas to ensure that the learned models satisfy some crucial properties, especially reliability and fairness (in the sense that the models need to apply to individuals in an equitable manner). To achieve these important objectives, we shall not “open up the black box” and try understanding its underpinnings. Rather, we discuss broad methodologies that can be wrapped around any black box to produce results that can be trusted and are equitable. The bulk of the talk will be about causal predictive inference. That is, answering questions of the following type: predict with confidence what the outcome would have been if a patient had not been treated.