🟠 Pricing & Demand

Price elasticity, revenue optimization, and the causal experiments behind pricing decisions.

Pricing is where causal inference stops being an academic exercise and starts moving money. Every price change is a treatment. Every demand response is a causal effect. The methods on this page turn that intuition into decisions defensible in front of a commercial team.

This arc reuses the vocabulary of the causal-inference arc and applies it to elasticity, demand, revenue, and marketplace dynamics.

Posts in this series

Price Elasticity Estimation: From OLS to Causal Methods

Why naive log-log regressions on pricing data lie, and what to do about it. A practitioner’s guide from OLS and fixed effects to IV, hierarchical Bayes, and double machine learning, with full R implementation.

Jul 13, 2026

74 min

Why Raising Prices Can Reduce Revenue in Two-Sided Markets

A practitioner’s walkthrough of two-sided market pricing: why single-sided elasticity models mislead, the chicken-and-egg problem for launch platforms, three modelling approaches (structural, reduced-form, Bayesian), and the review checklist for marketplace pricing analyses.

Aug 31, 2026

62 min

Dynamic Pricing Is Not Revenue Management. Confusing Them Is How Markets Get Worse for Everyone.

A practitioner’s walkthrough of pricing systems: the fifty-year revenue management foundation, the four legitimate extensions modern dynamic pricing added, the five failure modes where the modern wave went off the rails, a four-regime typology of the pricing problem, the causal-inference bridge to off-policy evaluation, a five-layer system architecture, an R simulation of classical dynamic programming versus naive reinforcement learning under a capacity constraint, and a review checklist for pricing-system architectures.

Sep 14, 2026

79 min