External Course

Causality Models, Reasoning and Inference: Causal Data Science Course

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.

Probabilities of Causation: Three Counterfactual Interpretations and their Identification

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

Performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection in the population

DSA ADS Course - 2021

Performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection in the population

COVID19, Public Policy, Health Policy, SARS-CoV-2, RT-PCR Test, Infectious Potential, Laboratory Quality Assurance, Dr. Kary Mullis, Cycle Threshold Values

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

DSA ADS Course - 2021

Causal Data Science, Machine Learning, Individual-level Optimal Causal Interventions, Causal Models

Discuss statistical causal inference methods.

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

Abstract

CausaLM: 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

Modelling Linguistic Causation

DSA ADS Course - 2021

Causal Data Science, Causal Inference, Linguistic Causation, Structural Equation Modelling

Discuss causative linguistic expressions, how causal relations are expressed in natural languages, and Structural Equation Modelling (SEM).

Modelling Linguistic Causation - 2021

Abstract

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

DSA ADS Course - 2021

DoWhy, Causal Assumptions, Causal Inference, Causal Data Science, Machine Learning

Discuss successful application of causal inference techniques.  As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for decision-making.

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