Causality

May, 2023

Abstract

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss causality, causal reasoning, causal inference, and machine learning algorithms.

Discuss causality and machine learning - contrast with statistical correlations.

The Seven Tools of Causal Inference, with Reflections on Machine Learning - March, 2019 By Judea Pearl

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss causality for machine learning.

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models.

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss algorithms for causal reasoning in probability trees.

Present concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events.

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability.

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events.

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.

2020

DSA ADS Course - 2021

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.

DSA ADS Course - 2021

Machine Learning, Causality, Causal Models, Knowledge Representation

Discuss “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. 

Radical Empiricism and Machine Learning Research - May, 2021 by Judea Pearl

Abstract