A soft introduction to causal inference

‘Correlation does not imply causation’ is a famous saying in statistics. The goal of many (scientific) studies is to draw conclusions on whether a so-called “treatment” has some effect or not, e.g. assigning a drug, or showing homepage A or homepage B to a visitor. As mentioned, it is in general not sufficient to observe statistical association of phenomena to infer causal relationships, but it is possible to make assumptions allowing for it. In this talk I want to define causal effects and what is necessary for causal inference.

References

[Her19C]

Causal Inference: What if, Miquel A. Hernan, James M. Robins.

Dec 2019

Causal inference is a complex scientific task that relies on combining evidence from multiple sources, and on the application of a variety of methodological approaches. Causal Inference: What If is an introduction to causal inference when data are collected on each individual in a population. The book is divided into three parts of increasing difficulty: causal inference without models, causal …

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