Causal mediation analysis in instrumental-variables regressions

Abstract

In this article, we describe the use of ivmediate, a new command to estimate causal mediation effects in instrumental-variables settings using the framework developed by Dippel et al. (2020, unpublished manuscript). ivmediate allows estimation of a treatment effect and the share of this effect that can be attributed to a mediator variable. While both treatment and mediator can be potentially endogenous, a single instrument suffices to identify both the causal treatment and the mediation effects.

Rural schools as effective hubs for agricultural technology dissemination: experimental evidence from Tanzania and Uganda

“…improved OFSP nutrition knowledge in our case). Following Hicks and Tingley (2011); Imai et al (2011) andDippel, Ferrara andHeblich (2020), let M(t) denote the potential value of the mediator under treatment status T = t. Similarly, let Y (t, m) be the potential outcome (i.e. OFSP adoption and consumption in our case) if the treatment and mediating indicators assume t and m, respectively.…”

“…where t = 0;1 indicating treatment status. Equation (3) shows that fixing T = t, the IE measures the expected change in Y when the value of the mediator changes from M(t 0 ) to M(t 1 ), while the DE is simply the share of the TE that does not operate through M. That is, fixing T = t, IE(t) measures the change in Y corresponding to a change in the mediator from the value that would be realised under the counterfactual condition, M(t 0 ), to the value that would be observed under the treatment condition, M(t 1 ) (Dippel, Ferrara and Heblich, 2020;Hicks and Tingley, 2011;Imai et al, 2011). Thus, the IE will be zero when the treatment has no effect on the mediator so that E[M(t 1 ) − M(t 0 )] = 0.…”

“…Second, following the approach of Dippel, Ferrara and Heblich (2020), we have attempted to identify the IE of nutrition knowledge without imposing Assumption (4.1) using an instrumental variable (IV) estimation strategy. As explicated in Dippel, Ferrara and Heblich (2020), the IV estimation strategy identifies IE consistently so long as the source of endogeneity is confounders that jointly influence T and M but not T and Y.…”

Resource dependence and the causes of local economic growth: An empirical investigation*

“…We again instrument for ΔRD i as in (1). As we do not have separate instruments to address the possible endogeneity of our potential causal channels in ( 7), we first assume them to be exogenous, then also use Dippel et al (2020) causal mediation analysis under the Stata IVMediate module. Causal mediation analysis decomposes the effect of ΔRD i on ΔLnGRDP i into a direct and indirect effect via a single causal channel when both the treatment and channel are potentially endogenous, using only a single instrument for both.…”

“…As a robustness check for Step 3, we allow for endogeneity of our potential causal channels along with ΔRD i without new instruments using Dippel et al (2020) causal mediation analysis. As mentioned, this approach jointly estimates the indirect effect of an ΔRD i measure on ΔLnGRDP i via one causal channel at a time (using only a single abundance instrument), as well as ΔRD i s direct effect net of that indirect effect, which combine as a total effect.…”