Overview

This page organizes my work on causal inference — from foundational concepts to applied modeling in marketing, experimentation, and decisioning.

The focus is on estimating causal effects, understanding heterogeneity, and making better decisions under uncertainty.

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Foundations: From Association to Causation

Core concepts behind causal reasoning and why correlation is not enough.

• Association vs Causation: A Minimal Potential Outcomes Demo
  (Code)

• Causal Graphs, Confounding, Colliders, and Selection Bias
  (Code)

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Randomized Experiments

Understanding causal effects through controlled experiments.

• Email Subject Line Experiment: Randomized Experiments and Statistical Review
  (Code)

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Observational Causal Inference

Estimating causal effects when randomization is not available.

• Propensity Scores in Practice: IPW and Doubly Robust Estimation
  (Code)

• Bank Marketing — Causal Linear Regression
  (Code)

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Heterogeneous Treatment Effects (CATE & Uplift)

Understanding who responds, not just whether something works.

• Finding Who Actually Responds: CATE-Based Targeting
  (Code)

• Meta-Learners for Heterogeneous Treatment Effects
  (Code)

• Advanced CATE Estimation Methods
  (Code)

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Bayesian & Decision-Focused Causal Modeling

Incorporating uncertainty into decision-making.

• Bayesian Models for Campaign Decisioning: Handling Uncertainty in Real Data
  (Code)

• Double Machine Learning Finds Segments, Bayesian Decides Which Ones to Trust
  (Code)

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Structural Causal Models

Moving from treatment-effect estimation toward mechanism-level reasoning and counterfactual analysis.

• Building Structural Causal Models: An End-to-End Workflow with DoWhy, EconML, and Refutation Tests
  (Code)

Key ideas:

• Moving beyond average effect estimation
• Making assumptions explicit with causal graphs
• Using refutation and counterfactual reasoning
• Connecting estimation to intervention and policy analysis

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How to Navigate

If you’re new to causal inference:

1. Start with Association vs Causation
2. Move to Experiments
3. Then explore Observational methods
4. Finally, go into CATE and Bayesian decisioning

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Why This Matters

In real-world systems — marketing, product, and credit — decisions are not made on predictions alone.

They require understanding:

• What causes outcomes
• What works for whom
• How uncertain those effects are

This collection reflects that journey from theory → estimation → decisioning.