# Causal Inference

## An Introduction to Causal Inference

Causal Inference, Directed Acyclic Graphs, Counterfactuals, D-separation, Do-calculus, Simpson’s Paradox, Structural Causal Model

Introduction to Causal Inference - Fabian Dablander

September, 2020

Abstract

## Causal Inference and the Data-fusion Problem

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

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

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

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

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.

## Causes of Effects and Effects of Causes

2014

Abstract

This paper summarizes methods that were found useful in estimating the probability that one event was a necessary cause of another, as interpreted by law makers. We show that the fusion of observational and experimental data can yield informative bounds which, under certain circumstances, meet legal criteria of causation. We further investigate the circumstances under which such bounds can emerge, and the philosophical dilemma associated with determining individual cases from statistical data.