Data-adaptive causal inference methods for effect modification

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"Causal inference" is the study of cause and effect. For instance, we may be interested in knowing whether a treatment has an impact on lessening the symptoms of a disease. Statistically speaking, we can estimate the impact of treatments on average in a given group of individuals. But it may be that some individuals are more greatly impacted by a treatment than others. Therefore, we may also be interested in evaluating which characteristics of the individuals in the group modify the effects for the individual. This knowledge can guide decision making by helping us identify which individuals will most benefit from a treatment, and which individuals will not obtain a benefit and may even be harmed by a treatment. My research program is focused on the development of statistical methods for identifying individual characteristics that can help predict effects for the individual. The data that I use come from studies where treatments were not randomly assigned to individuals (called "observational studies"). This leads to a major challenge - there are often relationships between treatments that individuals "choose" and their general state. Therefore, I focus on statistical approaches that can robustly correct for these relationships in order to estimate effects. Another focus in my research is the development of methods that can "automatically" discover the characteristics that modify effects in individuals. Essentially, this involves letting an algorithm crunch the data and determine which characteristics are related to different sized effects. My specific contributions involve the development of methods for different data structures and types. For example, specific challenges arise when individuals change their treatments over time or when characteristics can influence effects in complex ways. Because they are generally useful for any investigation of individual effects, the methods I develop can be used in health, economics, policy and engineering research across academia, government, and industry.