Causal Data Science

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.

Causal Inference and the Data-fusion Problem

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.

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