Alexandre Andorra
š¢ Exciting news! Join us for the third Learning Bayesian Statistics Modeling Webinar!
š Causal Inference and Propensity Scores Webinar š
šļø Date: March 1, 2024
ā° Time: 5:30pm (17:30)
š Timezone: UTC
š Introduction: 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.
š Agenda:
- Propensity Score Estimation: Discover how to calculate propensity scores and their significance in causal analysis. š
- Bayesian Analyst's Perspective: See how propensity score weighting can enrich your Bayesian models with valuable information. š§®
- Machine Learning & Causal Inference: Explore the application of propensity scores in debiasing machine learning for causal inference. š¤
- Contrast: We'll highlight the differences between non-parametric BART models and simpler regression models in estimating propensity scores and causal effects. š
š Datasets:
- NHEFS Data from Causal Inference: What If (Hernan et al., 2020).
- Patient-focused Data from Bayesian Nonparametrics for Causal Inference and Missing Data (Daniels et al., 2023).
š 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. āļø
- Propensity Scores in Selection Effect Bias: Learn how to apply propensity scores to tackle selection bias head-on. š”ļø
- Debiased/Double ML for ATE Estimation: Discover how to use Debiased/Double Machine Learning to estimate the Average Treatment Effect (ATE). š
- Mediation Analysis: Dive into mediation analysis and estimate Direct and Indirect Effects. š
š Assumption Escalation:
As we delve deeper into the webinar, you'll witness how different levels of assumptions are required for valid causal inference under various confounding threats. š§©
š Don't miss this opportunity to enhance your causal inference skills and gain a deeper understanding of propensity scores! š
šļø Our guest speaker, Nathaniel Forde, is a data scientist specialising in probabilistic modelling 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.
šļø Limited spots available! Register now to secure your seat, and don't miss your chance to interact live with our speakers. If you're a Patron of the Learning Bayesian Statistics podcast, you can submit questions in advance.
š As a bonus, patrons will enjoy early access to the webinar recordings.
š„ Feel free to invite your friends and colleagues to this open webinar. Spread the word and let's learn together! š¤ Just share them the Topmate registration link.
Can't wait to see you there! Best Bayesian wishes š
Useful references