Exploring the Role of Confounding Bias, Selection Bias, and Measurement Bias in Causal Inference — Previous articles explored The Traps of Blindly Trusting Data: Simpson’s Paradox, An Intuitive Introduction to Causal Inference, and then Visualizing Causality Using Causal Graphs This article delves into d-separation rules and explains different types of biases in causal inference: confounding bias, selection bias, and measurement bias, with examples.