Causal inference
This page highlights selected studies and resources that illustrate how causal inference is applied across industries directly relevant to consulting in retail, manufacturing, logistics, and mobility. For more resources and tools on core themes of my work, see Resources & tools.
Why It Matters for Business & Consulting
- Retail & Marketing — Estimate whether ads or promotions really drive sales.
- Digital Platforms — Run trustworthy A/B tests that account for network effects.
- Manufacturing & Supply Chains — Identify process improvements and manage risk under uncertainty.
- Transportation & Infrastructure — Evaluate mobility, safety, and congestion policies with robust methods.
Advertising & Digital Platforms
- eBay Ads Experiments (Blake, Nosko & Tadelis, 2015) — Geo-experiments on advertising ROI.
- Facebook Ads RCTs (Gordon et al., 2019) — 15 large-scale randomized trials contrasting observational vs causal effects.
- Measuring Ad ROI (Lewis & Rao, 2015) — Field experiments showing uncertainty in digital ad impact.
Experimentation & Marketplaces
- Experimental Designs in Marketplaces (Bajari et al., Statistical Science, 2023) — Overview of experimental methods for online platforms.
- Trustworthy Online Controlled Experiments (Kohavi et al., 2020) — A/B testing playbook from Microsoft, Google, and LinkedIn.
- Statistical Challenges in Online Controlled Experiments (Larsen et al., The American Statistician, 2023) — Review of methodological challenges in large-scale A/B testing.
Manufacturing & Supply Chains
- Causal ML for Supply Chain Risk Prediction & Intervention Planning (Wyrembek et al., International Journal of Production Research, 2025) — Application of causal ML to predict risks and design interventions.
Transportation & Infrastructure
- Congestion Taxes & Ridesourcing (Liang et al., 2022) — Difference-in-differences study of congestion taxes in Chicago.
- Ride-hailing & Congestion (Dhanokar & Burtch, 2021) — Uber’s impact on traffic is context-dependent, with weekday pooling but weekend crowding.
- Causal Inference for Transport Research (Graham, 2025) — Comprehensive review of causal methods in transport, with case studies, simulations, and R code. ScienceDirect