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How to Control Causal Inference Bias?
Sharpen your research skills with practical techniques to overcome bias in causal inference.
Previous articles explored The Traps of Blindly Trusting Data: Simpson’s Paradox, An Intuitive Introduction to Causal Inference, and Visualizing Causality Using Causal Graphs. Then, we discussed How Biases Impact Causal Inference.
This article will explore techniques for controlling or adjusting the biases that impact Causal Inference before finally learning how to build Causal DAG.
Recap of Different Biases in Causal Inference
Causal inference is the process of understanding cause-and-effect relationships. It focuses on inferring the causal effect of an intervention, treatment, or exposure on a particular outcome.
Causal graphs are Directed Acyclic Graphs (DAGs) used to model relationships between variables in causal inference. They depict expert knowledge and assumptions about how variables interact, aiding in understanding observations and drawing accurate conclusions about cause and effect.
Three different types of biases occur in causal inference: Confounding bias, Selection bias, and Measurement Bias.
Confounding Bias
Confounding is the bias that arises when the treatment and the outcome share a common cause.
