Data-adaptive learning in causal inference for high-dimensional data structures

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Causal inference is the study of the effects of interventions on an outcome. For instance, we might be interested in evaluating the consequences of a carbon reduction environmental initiative or comparing two different treatment approaches for managing an illness. Mathematically, differences in effects are easy to compare when we perform a randomized experiment. Randomized experiments involve the recruitment of study participants/units who are then randomly allocated one of two interventions. Since the allocation is random, we can compare the results for each intervention group at the end of the study, and any difference between the two groups should be due to the difference in intervention. In particular, due to randomization, the two groups of subjects should look very similar in their characteristics before the intervention is applied. However, in the absence of a randomized experiment, subjects are not randomly assigned to intervention groups, but are differentially exposed to interventions, potentially based on their predispositions. This type of data (called observational since we are not experimentally intervening) is much more complicated to analyze.