# External Course

## 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.

## Counterfactual Data-Fusion for Online Reinforcement Learners

Causal Reinforcement Learning, Counterfactuals, Counterfactual Data-Fusion, Online Reinforcement Learners

Discuss counterfactuals, risks of data fusion, causal reinforcement learning and fusion of observational and experimental data.

Counterfactual Data-Fusion for Online Reinforcement Learners - June, 2017

Abstract

## 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.

## A TALE OF TWO SCIENTIFIC PARADIGMS: CONFLICTING SCIENTIFIC OPINIONS ON WHAT “FOLLOWING THE SCIENCE” MEANS FOR SARS-COV-2 AND THE COVID-19 PANDEMIC

August, 2021

COVID19, Public Policy, Health Policy, Modeling, Model-driven Science, Empirically-driven Science, Lockdowns, Non-pharmaceutical Interventions, NPIs

Discuss 2 paradigms: Model-driven Science vs. Empirically-driven Science

## 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.

March, 2020