---
title: "Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces"
url: https://daily.dev/posts/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-xjli28ikv
source_url: https://eng.lyft.com/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-9cb06d628f2d
type: article
source: "Lyft Engineering"
published: 2026-03-25T14:15:50.022Z
updated: 2026-03-25T14:16:20.762Z
tags: ["data-science", "ab-testing"]
reading_time: 9
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.

# Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces

**[Lyft Engineering](https://daily.dev/sources/lyfteng)** · 9 min read · 0 upvotes · 0 comments

## Summary

Lyft's Foundational Models team describes a methodology for estimating long-term, market-mediated effects of pricing and incentive decisions in a two-sided marketplace. Standard A/B tests can't capture these effects because policy changes ripple through supply and demand in complex ways. The framework uses a two-step surrogacy approach: first, residualized regression models map policy changes to shifts in negative user experiences (wait times, surge, driver idleness); second, doubly-robust AIPW causal estimation maps those experience shifts to future behavioral outcomes via a surrogacy index. Both steps are validated with switch-back and user-split experiments respectively. Finally, direct and market-mediated long-term effects are combined and verified end-to-end using region-split experiments, with a forward-selection algorithm to optimize treated/control region matching. The result is a continuously calibrated causal engine for budget allocation and scenario planning.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://eng.lyft.com/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-9cb06d628f2d>

## Similar posts on daily.dev

- [Product Experimentation at Scale: How Airbnb, Netflix, Lyft, and Uber run Causal Inference on LLM-Based AI Features](https://daily.dev/posts/product-experimentation-at-scale-how-airbnb-netflix-lyft-and-uber-run-causal-inference-on-llm-ba-jo46utauq) · freeCodeCamp · 2 upvotes · 0 comments
- [When Can LLMs Replace Humans in A/B Tests?](https://daily.dev/posts/when-can-llms-replace-humans-in-a-b-tests--joccfpevz) · Spotify Labs · 6 upvotes · 0 comments
- [A/B test models in production](https://daily.dev/posts/a-b-test-models-in-production-w0dbvgkzs) · Together AI · 0 upvotes · 0 comments
- [Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models](https://daily.dev/posts/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-r18xv6fnc) · Lyft Engineering · 0 upvotes · 0 comments
- [Product Experiment Counterfactual Methods for Estimating the Effects of AI Prompt Engineering](https://daily.dev/posts/product-experiment-counterfactual-methods-for-estimating-the-effects-of-ai-prompt-engineering-ggrmaz0ix) · freeCodeCamp · 1 upvotes · 0 comments

---

Tags: [#data-science](https://daily.dev/tags/data-science), [#ab-testing](https://daily.dev/tags/ab-testing)

[View this post on daily.dev](https://daily.dev/posts/beyond-a-b-testing-using-surrogacy-and-region-splits-to-measure-long-term-effects-in-marketplaces-xjli28ikv)