Genetically adjusted propensity score matching: Closing the gap between non-experimental designs and true experiments in the social sciences.

Objectives: Social scientists employ various statistical techniques to approximate the causal association between two interrelated constructs. Although these methodologies have proven particularly useful for the advancement of knowledge, the limitations associated with preceding statistical techniques limit the ability of scholars to approximate causal associations within some conditions. As such, the current manuscript provides a new statistical technique designed to approximate causal associations. Methods: Genetically adjusted propensity score matching (GAPSM) represents an innovative iteration of propensity score matching (PSM) designed to integrate environmental and genetic factors into the matching process. As proposed, through the implementation of polygenic risk scores, future scholars can estimate genetically adjusted propensity scores (GAPS) through the implementation of two distinct statistical processes. To demonstrate the validity of the GAPSM approach, the current study employs simulation analyses to compare the point estimates derived from a post-GAPSM model, to the point estimates derived from a post-PSM model and a MZ difference score model. Results: The results of the simulation analyses demonstrated that when more robust environmental measures are introduced into the GAPSM approach and a larger amount of the variance in a treatment condition is explained by environmental factors, post-GAPSM models approach the true point estimate more closely than the point estimates derived from post-PSM models and MZ difference score models. Conclusions: Overall, the findings demonstrate that the GAPSM approach can prove useful when assessing the causal effects of treatment conditions on subsequent phenotypes by adjusting for observed environmental and genetic factors. Within the social sciences, this method could provide substantive advancements in our understanding of causal effects.