# Causal Analysis

## Statistical Modeling The Two Cultures

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss the two cultures of statistical modeling according to Leo Breiman in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

## Probabilities of Causation: Three Counterfactual Interpretations and their Identification

Causal Analysis, Causality Inference, Counterfactuals

Discuss how data from both experimental and nonexperimental studies combined provide information that neither study alone can provide. Show that necessity and sufficiency are two independent aspects of causation, and that both should be invoked in the construction of causal explanations for specific scenarios.

1999 - Probabilities of Causation: Three Counterfactual Interpretations and their Identification by Judea Pearl

Abstract

## Causality Models, Reasoning and Inference: Causal Data Science Course

Causal Analysis, Causality Models, Causal Inference

Discuss causal analysis and how it applies to many different fields like health policy and medical practice, public policy, business decisions, statistics, artificial intelligence, economics, philosophy, cognitive science, and others. Review probabilistic, manipulative, counterfactual, and structural approaches to causation and apply simple mathematical tools for studying the relationships between causal connections and statistical associations.

## Causally Colored Reflections on Leo Breiman’s “Statistical Modeling: The Two Cultures”

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss Leo Breiman’s “Statistical Modeling: The Two Cultures”  in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

July, 2021

## Statistical Modelling in the Age of Data Science

Causal Machine learning, Double Machine Learning, Targeted Learning, Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

Discuss causal machine learning, double machine learning and targeted learning.

Statistical Modelling in the Age of Data Science - July, 2021

Abstract

## An Introduction to Causal Inference

Causal Inference, Directed Acyclic Graphs, Counterfactuals, D-separation, Do-calculus, Simpson’s Paradox, Structural Causal Model

Introduction to Causal Inference - Fabian Dablander

September, 2020

Abstract