Causal inference

4 posts

  • Why Adjusted Regression Coefficients Are Less Descriptive Than They Look

    10 min read Statistics Interactive article

    An interactive article about how regression adjustment works and the pitfalls of conditional associations in descriptive studies.

  • Heterogeneous treatment effects and homogeneous outcome variances

    6 min read R

    Recently there has been a couple of meta-analyses investigating heterogeneous treatment effects by analyzing the ratio of the outcome variances in the treatment and control group. The argument made in these articles is that if individuals differ in their response, then observed variances in the treatment and control group in RCTs should differ. In this post I explore this argument and provide a counterargument.

  • Mediation, confounding, and measurement error

    7 min read R

    In this post, I give a brief simulation-based example of how confounding and measurement error impacts the estimation of direct and indirect effects in a mediation analysis.

  • Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

    11 min read R

    Non-randomized comparisons are common in RCTs. In this post I show some examples of confounding and collider bias, using treatment adherence as an example. I present a small simulation study that show that common regression models used in clinical psychology, makes little sense, and that Bayesian instrumental variable regression can be easily fit using the R package brms.