A/B testing is a foundational analytics tool, but many product decisions fail because results are noisy, biased, or misinterpreted.
This project implements an end-to-end experimentation and causal inference framework that estimates true treatment impact, going beyond simple p-values and dashboards.
The emphasis is on statistical rigor, interpretability, and decision support.
Common challenges in experimentation:
- High variance in metrics
- Selection bias and confounding
- Over-reliance on p-values without context
Goal: Build a reusable analytics framework that helps teams understand whether an intervention worked, by how much, and why.
The platform implements multiple complementary approaches:
Baseline comparison of treatment vs control groups with clear assumptions.
Uses pre-experiment data to reduce variance and increase statistical power, enabling faster and more reliable conclusions.
Estimates treatment impact by comparing pre- and post-period changes between groups, helping control for time-based effects.
Time-series-based causal inference to estimate counterfactual outcomes and quantify uncertainty.
- Ingest experiment data
- Preprocess and validate assumptions
- Apply variance reduction (CUPED)
- Estimate impact using causal methods
- Summarize results for stakeholders
This mirrors how advanced analytics teams support product and growth decisions.
Rather than surfacing raw statistics, the framework produces:
- Effect size estimates
- Confidence intervals
- Plain-English interpretations
The goal is to support confident decision-making, not statistical theater.
This platform enables teams to:
- Make faster experiment decisions with less noise
- Reduce false positives and false negatives
- Understand causal impact instead of correlation
It reflects how experimentation is handled in mature, data-driven organizations.
- Python
- Pandas
- Statsmodels
- Causal inference techniques
- SQL-style aggregations