Automation and design in observational causal inference

Abstract: The use of automated procedures has recently become popular in the causal inference literature. Naive implementations of automatic procedures stand in contrast to the perspective advocated in Imbens and Rubin's Causal Inference. Imbens and Rubin suggest that researchers should make modelling decisions informed by subjective knowledge. We make use of simulated data to compare Imbens and Rubin's approach to naive implementations of two automatic procedures: Genetic Matching and Entropy Balancing. In addition we perform a small Monte Carlo simulation, based on one of the simulated data sets. Using the simulated data sets and the Monte Carlo simulations, we illustrate and explore benefits and drawbacks of the different approaches. We argue that there are benefits to make use of design-decisions grounded in subjective knowledge.