Causal Analysis

DSA ADS Course, 2023

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.

DSA ADS Course - 2021

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

DSA ADS Course - 2021

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.

September, 2018

Abstract

DSA ADS Course, 2022

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

DSA ADS Course, 2022

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

2019

DSA ADS Course - 2021

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

DSA ADS Course - 2021

Causal Inference, Data-fusion, Causal Analysis, Causal Data Science, Counterfactuals, Selection Bias

Discuss concepts and techniques of different approaches to causal analysis, the curse of big data, data fusion and bias challenges. Discuss biases such as: confounding, sampling selection, and cross-population biases, along with a general, potential mitigation nonparametric framework for handling biases.

Discuss appropriate use of counterfactuals in causal analysis and risk of reasonable inference vs. unreasonable inference.

June, 2021

Abstract