Causal effect of ranking position on clicks
Causal Inference
Recommendations
AIPW
Causal ML
A notebook-driven causal inference project estimating whether top-3 recommendation exposure increases click probability in MIND impression logs.
Decision Question
Does placing a news item in the top 3 recommendation positions cause more clicks, or are top-ranked items simply more relevant and therefore more likely to be clicked anyway?
Causal Setup
- Treatment: item appears in the top 3 recommendation positions.
- Outcome: click on the displayed item.
- Adjustment: user history, item metadata, slate size, time context, and item exposure features.
Methods
- Propensity modeling and IPW
- Doubly robust / AIPW estimation
- Heterogeneous effects
- Policy simulation
- ML nuisance models and EconML extensions
Portfolio Takeaway
The project demonstrates the full applied causal workflow: confounding checks, adjustment, doubly robust estimation, heterogeneity, policy simulation, and sensitivity analysis for a ranking decision.
Selected Figures
Notebook Sequence
The links below open rendered HTML versions of the notebooks. The source .ipynb files are kept in the matching folder under notebooks/projects/.
- 01 - MIND Rank Position EDA
- 02 - Propensity Modeling And IPW
- 03 - Doubly Robust Estimation
- 04 - Heterogeneous Treatment Effects
- 05 - Policy Simulation
- 06 - Sensitivity And Limitations
- 07 - ML Nuisance Models With LightGBM And XGBoost
- 08 - EconML Causal ML Estimators
- 09 - Final Report Figures And Tables
Generated Artifacts
Limitations
These are notebook-driven causal analyses, not production guarantees. Each project should be read with its identification assumptions, support diagnostics, measurement choices, and sensitivity checks in view.