Causal Inference

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.

A Critical View of the Structural Causal Model

DSA ADS Course - 2021

Causal Inference, Causality, Structural Causal Model, SCM

Discuss causality vs. correlation - causality vs. statistics in machine learning. Review techniques for attempting to find true causality. 

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related.

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