Improving Insights by Utilizing Causal Inference Methods

Explore causal inference methods for enhancing data science model insights in this 40-minute talk from the Data Science Festival. Delve into the challenges of understanding incremental improvements without experimentation, and learn how traditional exploratory data analysis techniques can lead to biased conclusions. Discover how causal inference methods can de-bias data and approximate the value of incremental improvements. Gain insights into the use and democratization of propensity score matching and regression discontinuity for accelerating insights. Examine the challenges related to omitted variable bias and sensitivity analysis, equipping yourself with advanced techniques to improve your data science models and decision-making processes.