Estimate the proximal and distal causal effects of a just-in-time intervention component on a continuous outcome using data from a Micro Randomized Trial

About This Code

This code is used to estimate the causal effect of a just-in-time intervention component on the mean of a continuous proximal or distal outcome using data from an MRT.

How can a behavioral scientist use this code?

Behavioral intervention scientists can use this code to make statistical inferences about the proximal and lagged causal effects of a JITAI intervention component on a continuous outcome. This code can also be used by behavioral intervention scientists to assess how the causal effect of a just-in-time intervention component is moderated by time-varying factors.

What method does this code implement?

This code implements an easy-to-use, weighted least squares regression estimator for assessing causal effects of just-in-time components in an MRT. In an MRT, the weights (which are potentially impacted by prior treatment) are known, by design.

Access Links

View Code on GitHub
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