External Course

Pfizer Adverse Events Report

DSA ADS Course - 2022

COVID19, Public Policy, Health Policy, mRNA Vaccines, Vaccines, Clinical Trial Design, Evidence Hierarchy

This is a FOIA document of the first 2.5 months after the vaccine roll-out. Discuss clinical trial design and hierarchy of evidence in context of mRNA vaccines.

5.3.6 CUMULATIVE ANALYSIS OF POST-AUTHORIZATION ADVERSE EVENT REPORTS OF PF-07302048 (BNT162B2) RECEIVED THROUGH 28-FEB-2021

The Pfizer Inoculations Do More Harm Than Good

DSA ADS Course - 2022

COVID19, Public Policy, Health Policy, mRNA Vaccines, Vaccines, Clinical Trial Design, Evidence Hierarchy

Discuss clinical trial design and hierarchy of evidence in context of mRNA vaccines.

The Pfizer Inoculations Do More Harm Than Good - December, 2021

See also video: https://rumble.com/vqx3kb-the-pfizer-inoculations-do-more-harm-than-good.html

How bad data quality can turn a simulation into a dissimulation that shapes the future

DSA ADS Course, 2022

Data Quality, Black Box Models, Bad Models, Origins of SARS-CoV-2, Epidemiology, Non-pharmaceutical Interventions, Mitigation Strategies, COVID19, Health Policy, Public Policy

Review data quality evaluations, bad model design, policy decision-making based on data science, black box models, unwarranted assumptions in models, and evidence based policy making with near real-time data.

Causally Colored Reflections on Leo Breiman’s “Statistical Modeling: The Two Cultures”

DSA ADS Course, 2022

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss Leo Breiman’s “Statistical Modeling: The Two Cultures”  in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

July, 2021

Statistical Modelling in the Age of Data Science

DSA ADS Course, 2022

Causal Machine learning, Double Machine Learning, Targeted Learning, Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

Discuss causal machine learning, double machine learning and targeted learning.

Statistical Modelling in the Age of Data Science - July, 2021

Abstract

Statistical Modeling The Two Cultures

DSA ADS Course, 2022

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss the two cultures of statistical modeling according to Leo Breiman in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

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