Causal Models

May, 2023

Abstract

If X,Y,Z denote sets of random variables, two different data sources may contain samples from PX,Y and PY,Z, respectively. We argue that causal discovery can help inferring properties of the `unobserved joint distributions' PX,Y,Z or PX,Z. The properties may be conditional independences (as in `integrative causal inference') or also quantitative statements about dependences.

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 statistical causal inference methods.

Estimating individual-level optimal causal interventions combining causal models and machine learning models - 2021

Abstract

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

May, 2021

Abstract

November, 2020

See Code - Global COVID-19 Forecast Comparison

https://github.com/pyliu47/covidcompare