Mastering Causal Inference and Quasi-Experiments: Enhancing Your Understanding

Mastering Causal Inference and Quasi-Experiments: Enhancing Your Understanding

Introduction:

In the ever-evolving world of product management, running A/B tests is crucial to determining the effectiveness of new ideas. However, what happens when your PM forgets to run this essential experiment? In this informative series, we delve into the realm of quasi-experiments for causal inference, exploring the reasons behind their use and the nuances involved. We will also shed light on the limitations of quasi-experiments and propose a framework for heterogenous impact estimation to overcome potential bias. Whether you’re a seasoned product manager or just starting out, this series will equip you with the knowledge to evaluate the true impacts of your products and features effectively.

Full Article: Mastering Causal Inference and Quasi-Experiments: Enhancing Your Understanding

Your PM forgot to run an A/B test… what now?

Product managers often make bold claims about the impact of their latest feature launches. However, it is crucial to understand the methods of causal inference behind these claims. This article is the first in a series that will explore the use of quasi-experiments for causal inference, specifically focusing on the whys and hows of quasi-experiments and the nuances involved when applying approaches like PSM (Propensity Score Matching).

This article is Part 1 of n (depending on how much I end up rambling on) in a series of articles about using quasi-experiments for causal inference. Briefly, Part 1 will explain the whys and hows of quasi-experiments, as well as the nuances involved when applying approaches like PSM. In Part 2, I will talk more about the limitations of quasi-experiments and what you should be cautious about when making decisions based on them. I will also propose a framework for heterogenous impact estimation that can help overcome extrapolation bias. In Part 3… I’m still not sure yet.

You may also have come across other articles explaining Quasi-Experiments, but I’m still going to try explaining it my way. Give it a read.

Many product managers claim that their latest feature launches have led to impressive revenue increases. While this may sound great, it is important to go beyond surface-level acceptance and understand the principles of causal inference behind these claims.

According to ChatGPT, causal inference enables product teams to move beyond merely observing correlations in data and gain a deeper understanding of the causal mechanisms driving product performance. It is essential to distinguish between correlation and causality, as one does not imply the other.

Stay tuned for Part 2, where we will delve into the limitations of quasi-experiments and offer cautionary advice for making decisions based on them. We will also introduce a framework for heterogenous impact estimation that helps overcome extrapolation bias.

Curious to learn more about Quasi-Experiments? Give this article a read and discover a new perspective on the topic.

Summary: Mastering Causal Inference and Quasi-Experiments: Enhancing Your Understanding

In this article, the author discusses the importance of conducting A/B tests and the potential consequences of not running them. They explain that product managers often make claims about the positive impact of new features or products without proper causal inference. The author emphasizes the need to go beyond simple correlations and establish a deeper understanding of the causal mechanisms driving product performance. They also highlight the difference between correlation and causation and urge readers to be cautious when interpreting data. The article aims to provide a better understanding of causal inference and its significance in evaluating the impact of products on users and companies.

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