---
title: "A Guide on Estimating Long-Term Effects in A/B Tests"
url: https://daily.dev/posts/a-guide-on-estimating-long-term-effects-in-a-b-tests-cwzve155c
source_url: https://towardsdatascience.com/a-guide-on-estimating-long-term-effects-in-a-b-tests-9a3790501047
type: article
source: "Towards Data Science"
published: 2024-02-24T14:26:49.028Z
updated: 2024-05-09T09:06:08.895Z
tags: ["data-science", "statistics", "ab-testing"]
reading_time: 8
upvotes: 1
comments: 0
language: en
---

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# A Guide on Estimating Long-Term Effects in A/B Tests

**[Towards Data Science](https://daily.dev/sources/tds)** · 8 min read · 1 upvotes · 0 comments

## Summary

Short-term experiment results often differ from the long-term due to factors like heterogeneous treatment effects or user learning. Methods for identifying trends in long-term effects include visualization, ladder experiment assignment, difference-in-difference, random vs constant treatment assignment, and user 'unlearning'. The long-term effects can be predicted using auto-surrogate models that forecast the long-term outcome of the experiment.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/a-guide-on-estimating-long-term-effects-in-a-b-tests-9a3790501047>

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---

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

[View this post on daily.dev](https://daily.dev/posts/a-guide-on-estimating-long-term-effects-in-a-b-tests-cwzve155c)
