Title: FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference,
Abstract: A classical problem in causal inference is that of matching, where treatment
units need to be matched to control units. Some of the main challenges in
developing matching methods arise from the tension among (i) inclusion of as
many covariates as possible in defining the matched groups, (ii) having matched
groups with enough treated and control units for a valid estimate of Average
Treatment Effect (ATE) in each group, and (iii) computing the matched pairs
efficiently for large datasets. In this paper we propose a fast method for
approximate and exact matching in causal analysis called FLAME (Fast
Large-scale Almost Matching Exactly). We define an optimization objective for
match quality, which gives preferences to matching on covariates that can be
useful for predicting the outcome while encouraging as many matches as
possible. FLAME aims to optimize our match quality measure, leveraging
techniques that are natural for query processing in the area of database
management. We provide two implementations of FLAME using SQL queries and
bit-vector techniques.