Causal Inference

May, 2023

Abstract

The current focus on real world evidence (RWE) is occurring at a time when at least two major trends are converging. First, is
the progress made in observational research design and methods over the past decade. Second, the development of
numerous large observational healthcare databases around the world is creating repositories of improved data assets to
support observational research.

February, 2020

Targeted Machine Learning for Causal Inference Based on Real-World Data

by Mark van der Laan

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.

May, 2023

Abstract

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 targeted advertising and trade-offs between advertising reach and targeting precision.

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 causative linguistic expressions and causal model explanation through counterfactual language models.

DSA ADS Course - 2021

Causal Data Science, Causal Inference, Linguistic Causation, CausaLM, Causal Model Explanation, Counterfactual Language Models, Machine Learning

Discuss causative linguistic expressions and causal model explanation through counterfactual language models.

CausaLM: Causal Model Explanation Through Counterfactual Language Models - 2021

Abstract