# Correlation Lied to Us: Rethinking Product Impact with Causal Inference

DevFeed: [Correlation Lied to Us: Rethinking Product Impact with Causal Inference](<https://devfeed.tech/articles/correlation-lied-to-us-rethinking-product-impact-with-causal-inference-20383.md>)

Original publisher: [Read original article](<https://tech.olx.com/correlation-lied-to-us-rethinking-product-impact-with-causal-inference-5ba47181f7c5?source=rss----761b019b483f---4>)

Author: Enderson Santos

Published: 2026-08-04T15:31:01Z

Content type: article

Language: en

Sources: [OLX](<https://devfeed.tech/sources/olx.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>)

Tags: [causal-inference](<https://devfeed.tech/tags/causal-inference.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [experimentation](<https://devfeed.tech/tags/experimentation.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product-analytics](<https://devfeed.tech/tags/product-analytics.md>)

## AI overview

The article examines why higher-tier OLX exposure packages appeared to perform worse than cheaper packages in some cases. It explains that sellers self-select packages, making direct package-level comparisons potentially misleading, and introduces causal inference as an approach to separate correlation from causation.

## Source excerpt

Introduction At OLX, professional sellers pay for higher-tier packages because they promise more exposure. More visibility, and, in theory, better results. But when we looked at the data, something unexpected happened. In some cases, ads published with premium packages appeared to perform worse than ads using cheaper packages. That raised an uncomfortable question: If higher-tier packages provide more exposure, shouldn't they consistently perform better? At first glance, there were several possible explanations. Perhaps the extra visibility weren't creating as much value as we expected. Perhaps ranking dynamics were offsetting the additional exposure. Or perhaps the package itself wasn't the real driver of performance. It was then that we started asking a different question: Were we measuring this correctly? More specifically, were the ads across different packages actually comparable in the first place? Answering that question turned out to be far more important than comparing package-level metrics. It forced us to rethink how we measure product impact in a marketplace environment and ultimately led us to a causal inference approach designed to separate correlation from causation. In this article, I'll walk through how we approached that problem, what we learned, and how comparing similar ads changed our understanding of the true value created by exposure products. Problem Definition To understand the challenge, it's important to first understand how package exposure works at OLX. Professional sellers self select into a package when publishing their ads. The difference between packages is largely defined by how many boosts an ad receives during its lifetime. For example, in the picture below we can see that Package 1 includes 1 boost on the period of 30 days, package 2 includes 2 boosts, package 3 includes 3 boosts, and package 4 includes 4 boosts all in the same period of 30 days. The business expectation is straightforward: more boosts should create more visibili