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

因果推理是研究干预措施对结果的影响。例如，我们可能对评估碳减排环境倡议的后果或比较两种不同的治疗方法来管理疾病感兴趣。从数学上讲，当我们进行随机实验时，效果的差异很容易比较。随机实验涉及招募研究参与者/单位，然后将其随机分配到两种干预措施之一中。由于分配是随机的，我们可以在研究结束时比较每个干预组的结果，两组之间的任何差异都应该是由于干预的差异。特别是，由于随机化，在应用干预之前，两组受试者的特征应该非常相似。然而，在没有随机实验的情况下，受试者并没有被随机分配到干预组，而是有差异地暴露于干预措施，这可能是基于他们的倾向。这种类型的数据（称为观测数据，因为我们没有进行实验干预）分析起来要复杂得多。

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.