--- title: "Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models" url: https://daily.dev/posts/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-r18xv6fnc source_url: https://eng.lyft.com/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-00853df009df type: article source: "Lyft Engineering" published: 2026-02-12T17:43:03.371Z updated: 2026-02-12T17:43:27.345Z tags: ["machine-learning", "data-science", "lyft", "ab-testing"] reading_time: 14 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models **[Lyft Engineering](https://daily.dev/sources/lyfteng)** · 14 min read · 0 upvotes · 0 comments ## Summary Lyft's data science team developed validation methods for Augmented Inverse Propensity Weighting (AIPW), a doubly robust causal inference model used when A/B testing isn't feasible. The platform requires rigorous confounder management with hundreds of features, applies propensity score corrections for downsampled data, and provides diagnostic scorecards checking propensity overlap and covariate balance. Validation against experimental ground truth from ride challenge programs revealed AIPW understates effects by 16% due to propensity trimming creating non-representative samples. The team added marginal sensitivity models and covariate comparison diagnostics to detect when hidden confounders or trimming compromise estimate reliability. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Product Experimentation with Doubly Robust Estimation: When Both Your Models Are Wrong in LLM Applications](https://daily.dev/posts/product-experimentation-with-doubly-robust-estimation-when-both-your-models-are-wrong-in-llm-applic-vxma9z7do) · freeCodeCamp · 1 upvotes · 0 comments - [Stop Blaming the Data: A Better Way to Handle Covariance Shift](https://daily.dev/posts/stop-blaming-the-data-a-better-way-to-handle-covariance-shift-2nbp2klza) · Towards Data Science · 3 upvotes · 0 comments - [Product Experimentation with Propensity Scores: Causal Inference for LLM-Based Features in Python](https://daily.dev/posts/product-experimentation-with-propensity-scores-causal-inference-for-llm-based-features-in-python-uju401omp) · freeCodeCamp · 4 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#lyft](https://daily.dev/tags/lyft), [#ab-testing](https://daily.dev/tags/ab-testing) [View this post on daily.dev](https://daily.dev/posts/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-r18xv6fnc)