Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms

Abstract

Political scientists are increasingly interested in assessing causal mechanisms, or determining not just if a causal effect exists but also why it occurs. Even so, many researchers avoid formal causal mediation analyses due to their stringent assumptions, instead opting to explore causal mechanisms through what we call intermediate outcome tests. These tests estimate the effect of the treatment on one or more mediators and view such effects as suggestive evidence of a causal mechanism. In this paper, we use nonparametric bounding analysis to show that, without further assumptions, these tests can neither establish nor rule out the existence of a causal mechanism. To use intermediate outcome tests as a falsification test of causal mechanisms, researchers must make a very strong but rarely discussed monotonicity assumption. We develop a way to assess the plausibility of this monotonicity assumption and estimate our bounds for two recent experiments that use these tests.