Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system.

How best to understand and characterize causality is an age-old question in philosophy. As such, one might expect that any discussion of causal inference would need to be framed in terms of subtle and …

Feb 21, 2022 · The key fact of causal inference is this: For any covariates in the system that can affect the measured outcome (directly or indirectly), you have to be sure that your treatment + control …

In this book, we stress the need to take the causal question seriously enough to articulate it, and to delineate the separate roles of data and assumptions for causal inference.

Aug 27, 2021 · Causal inference refers to the design and analysis of data for uncovering causal relationships between treatment/intervention variables and outcome variables. We care about causal …

Aug 11, 2025 · Causal inference emerges from this philosophical and statistical lineage, blending mathematical rigor with logical reasoning. It accepts that we live in a messy, interconnected world, …

Causal inference spans statistics, epidemiology, computer science, and economics. There are three languages to express causal assumptions and conclusions: potential outcomes, causal DAGs, and …

Unlike traditional statistical approaches that focus on correlation, causal inference aims to answer "what if" questions and understand how interventions affect outcomes.

Causal inference is the process of identifying and quantifying the causal effect of one variable on another.

The goal of causal inference is to establish that a cause-and-effect relationship exists. The fundamental challenge in causal inference is that you can't directly observe causal effects.