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• Bayesian Causal Inference & Propensity Scores

Bayesian Causal Inference & Propensity Scores

Abstract

In this webinar, we will explore the world of causal inference and how propensity scores can be a powerful tool. Learn how to estimate propensity scores and use them to tackle selection bias in your analysis.

🔍 Agenda

• Propensity Score Estimation: Discover how to calculate propensity scores and their significance in causal analysis. 📊
• Bayesian Analyst’s Perspective: See how propensity score weighting can enrich your Bayesian models with valuable information. 🧮
• Machine Learning & Causal Inference: Explore the application of propensity scores in debiasing machine learning for causal inference. 🤖
• Contrast: We’ll highlight the differences between non-parametric BART models and simpler regression models in estimating propensity scores and causal effects.

📈 Takeaways

• Propensity scores are useful in observational data to evaluate the effect of a treatment.
• Different weighting schemes, such as raw inverse weighting and doubly robust methods, can be used with propensity scores.
• Bayesian additive regression trees (BART) models offer flexibility in modeling propensity scores.
• Extreme propensity scores can be dealt with by removing individuals or using matching algorithms.
• Propensity scores can be used in regression modeling to estimate treatment effects. Propensity score adjustment, doubly robust estimation, and mediation analysis are methods used in causal inference.
• Beliefs and assumptions about the data generating process underlie these methodologies.
• It is important to be Bayesian in our approach to causal inference.
• Non-parametric estimation can help address miscalibration and overfitting risks.
• Understanding the causal structure and considering mediation effects are crucial in causal inference.

📜 Webinar Structure

• Non-parametric Approaches: We’ll showcase various non-parametric methods for estimating causal effects. Some are spot-on, while others can mislead you. ⚙️
• Propensity Scores in Selection Effect Bias: Learn how to apply propensity scores to tackle selection bias head-on. 🛡️
• Debiased/Double ML for ATE Estimation: Discover how to use Debiased/Double Machine Learning to estimate the Average Treatment Effect (ATE). 📈
• Mediation Analysis: Dive into mediation analysis and estimate Direct and Indirect Effects. 🔄

🎙️ Our guest speaker, Nathaniel Forde, is a data scientist specializing in probabilistic modeling for the study of risk and causal inference. He has experience in model development, deployment, multivariate testing and monitoring, and his academic background is in mathematical logic and philosophy.