Double machine learning, causal forests, TMLE, regression discontinuity, and synthetic difference-in-differences applied to pricing problems. When correlation isn't enough and you need to know what actually drives loss. 12 articles.
Ciganovic et al. (March 2026) show that standard DML cross-fitting leaks future information when your data is a time series. Their fix — Reverse Cross-Fitting — has direct impli...
Most UK insurers fit a logistic regression on PCW quote data and call it a demand model. It is biased in at least three distinct ways. Here is the causal structure that explains...
A new paper combines panel fixed effects, double machine learning, and instrumental variables. The headline result is not the estimator — it's that ML covariate adjustment frequ...
How to estimate a causally identified price elasticity from PCW quote data in Python, using commercial loading variation as an instrument and CatBoost nuisance models. The pract...
Li and Castro-Camilo (arXiv:2603.23309, March 2026) unify inverse probability weighting and extreme value extrapolation in a single estimating equation. Here is what it does, wh...
EconML is the standard Python library for causal ML. It was not built for insurance pricing, Poisson/Gamma exposure models, or the dual-selection bias problems specific to renew...
DoWhy is the most rigorous general-purpose causal inference library in Python — DAG specification, formal identification, refutation tests. It was not built for insurance pricin...
CausalForestDML separates causal price effect from risk-lapse correlation in UK motor renewal. insurance-elasticity - per-customer CATE and ENBP optimiser.
Doubly robust TMLE for insurance pricing with Poisson outcomes and exposure offsets. insurance-tmle - first Python library with the implementation AIPW lacks.
Causal Forests with Fixed Effects for UK insurance panel data. Rate change evaluation by segment - beyond before-and-after loss ratios. causalfe Python.
Bayesian Causal Forests for heterogeneous lapse effects in UK insurance pricing. Segment-level elasticity with posteriors - insurance-bcf wrapping stochtree.
Regression Discontinuity Design tests if UK motor risk drops at age 25. Exposure-weighted Poisson outcomes, geographic boundaries, Consumer Duty output.
A 12% rate increase on young motor drivers. An 8% lapse spike three months later. Here is how to tell whether the rate change caused it — using synthetic difference-in-differences.
Where double machine learning beats naive regression for insurance pricing — and where it does not. Benchmarks on 100,000-policy synthetic UK motor data with known ground truth....