Causality

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.

Forecasting for COVID19 has Failed

June, 2020

DSA ADS Course - 2021

Forecasting, COVID19, John Ioannidis, Public Policy, Health Policy, Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation

Forecasting is usually impossible in high causal density environments. Scenario planning with applied probability and adaptation to near real-time data is optimal strategy. Epidemic forecasting is usually a fools errand yet appropriate analysis of experience and historical precedent is helpful.

Review of Causal Inference in Forensic Medicine

March, 2020

DSA ADS Course - 2021

Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation

Causality - intuitive causation vs. probabilistic causation. Probabilistic causation is optimal yet discuss other techniques to seek true causation. Sometimes true causation is impossible to find and discuss optimal strategy under such circumstances.

Abstract

Suffcient Causes: On Oxygen, Matches, and Fires

DSA ADS Course - 2021

Suffcient Causes: On Oxygen, Matches, and Fires

Probability, Causality, Causal Inference, Counterfactuals, Suffcient Causes, Explanation, Abduction

2019

Abstract

We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufcient cause of another, and how counterfactuals emerge organically from basic scientifc knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.

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