Causal Machine Learning with CausalELM - JuliaCon 2024

Explore causal machine learning techniques using CausalELM in this 28-minute conference talk from JuliaCon 2024. Dive into the Potential Outcomes Framework and learn how to estimate causal effects through practical examples. Discover how to use double machine learning to assess the impact of 401(k) pension plan eligibility on net worth, and explore individualized treatment effects in the context of development aid's influence on Taliban attacks. Gain insights into Extreme Learning Machines and their role in CausalELM's ensemble methods. Compare CausalELM with other causal machine learning libraries and understand its unique tradeoffs. Suitable for those without extensive statistical background, this talk focuses on practical implementation rather than complex mathematical theory.

Causal Machine Learning with CausalELM | Colby | JuliaCon 2024