Identifying Causal Effects for Data Scientists
Published 4/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
What you’ll learn
Measure the causal impact of treatments for product analytics and policy evaluation
Think through causal inference problems from first principles
Map information you know about the world and the industry you work in into measures of causal impacts
Distinguish between what the data tells you about causal effects and what comes via your assumptions
Translate knowledge about how an industry or product works into bounds on treatment effects
Think carefully and deductively about making inference on treatment effects
Use the Conditional Independence Assumption to Identify Average Treatment Effects and the risks and potential bias from using the assumption
Use the parallel trends assumption to motivate difference-in-difference estimation, the downsides of the assumption, and a more robust alternative
Requirements
Basic statistics and probability. CDFs. Conditional Expectations.
Useful to have some background on linear regression.
Not a math-heavy course. Some algebra and basic properties of probability and expectations.
Description
Who this course is for
Data Scientists looking to expand their repertoire of methods for estimating causal effects
Data Scientists looking to improve their skills to derive novel approaches for measuring causal effects from first principles
People who know statistics but want to learn how to think carefully about identifying causal treatment effects
Anyone who wants to better understand how assumptions and data work together to identify treatment effects
