Causal Inference 101

4 min read

The parallel trends assumption (DiD)

What parallel trends means, how to assess it visually, and what to do when it is doubtful.

Parallel trends means the gap between treated and control outcomes would have stayed constant in the absence of treatment. We verify this using pre-treatment periods: trends should look similar before the intervention.

Event-study plots show coefficients by time relative to treatment. Pre-treatment coefficients near zero support credibility; sharp jumps at treatment time suggest an effect.

Run this method on your data — no Python

CausalLens runs matching, DiD, causal forests, DoWhy refutation, and more — with balance tables, sensitivity checks, and PDF export.