# quasi-experiment

Published articles for quasi-experiment.

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## Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models

DevFeed: [Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models](<https://devfeed.tech/articles/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-1244.md>)

Original publisher: [Read original article](<https://eng.lyft.com/trusting-the-untestable-validation-and-diagnostics-for-the-doubly-robust-models-00853df009df?source=rss----25cd379abb8---4>)

Author: Shima Nassiri

Published: 2026-02-12T17:07:13Z

Content type: article

Language: en

Sources: [Lyft Engineering - Medium](<https://devfeed.tech/sources/lyft-engineering-medium.md>)

Topics: [Estimator](<https://devfeed.tech/topics/estimator.md>), [data](<https://devfeed.tech/topics/data.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [aipw](<https://devfeed.tech/tags/aipw.md>), [doubleml](<https://devfeed.tech/tags/doubleml.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [experimentation](<https://devfeed.tech/tags/experimentation.md>), [lyft](<https://devfeed.tech/tags/lyft.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [models](<https://devfeed.tech/tags/models.md>), [quasi-experiment](<https://devfeed.tech/tags/quasi-experiment.md>), [rideshare](<https://devfeed.tech/tags/rideshare.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

The article explains how Lyft uses quasi-experimental methods to measure causal effects when randomized A/B tests are impractical. It focuses on Augmented Inverse Propensity Weighting (AIPW), a doubly robust estimator that uses outcome and propensity-score models to estimate treatment effects from non-randomized data.

### Source excerpt

written by Ross Chu and Shima Nassiri The Causal Frontier: Measurement Beyond Randomization The gold standard for determining the causal impact of a policy or product change at a company like Lyft is the A/B test (randomized experiment). By randomly assigning users to a treatment or control group, A/B tests inherently eliminate bias, providing clean estimates of the Average Treatment Effect (ATE). However, many critical business questions and large-scale initiatives simply cannot be randomized. This forces scientists to move past traditional experimentation and leverage quasi-experimental methods. We rely on non-randomized measurement in several key scenarios across Lyft: Partnerships and Policies: Assessing the incremental impact of a partnership (e.g., linking two company accounts) is often a non-randomized assignment. Since these collaborations require coordinated operational work across both companies and are typically announced or promoted broadly, this makes controlled randomization impractical. Long-Term Effect (LTE): Measuring effects that unfold over a long period, like the LTE of high prices on future rides, is typically handled by observational studies. Post-Launch Evaluation: Continuous monitoring of a policy after it has been fully rolled out requires a method that doesn't involve costly holdout groups or degradation tests. Biased Data: In cases where pre-existing experimental data is found to have an imbalance, a quasi-experimental approach can potentially leverage the biased data instead of requiring a costly rerun. Introducing Doubly Robust Models: Causal Inference Without Randomness To address these non-randomized measurement needs, Lyft relies on various quasi-experiment estimators. In this blog we specifically focus on using the Augmented Inverse Propensity Weighting (AIPW) model. This model was first established at Lyft to measure the impact of a negative user experience on future topline metrics like rides and bookings; AIPW is a form of doubly