Causal Inference Under Hidden Confounding: Learn advanced methods to uncover causal effects when key variables are unobserved

Aleksander Molak

$149.99

Video Oct 2025 48mins 1st Edition

Aleksander Molak

$149.99

Video Oct 2025 48mins 1st Edition

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Key benefits

Apply advanced causal inference methods to confounded data
Use IVs, front-door, and proximal methods for identification
Conduct robustness checks and sensitivity analyses in Python

Description

Causal Inference Under Hidden Confounding provides a practical roadmap for tackling one of the toughest challenges in causal analysis: estimating causal effects when not all confounders are observed. Hidden confounding is a reality in most real-world datasets, from healthcare and economics to policy and business, and naïve models risk misleading results. This resource equips you with the tools and frameworks to address these challenges. Beyond identification, you’ll explore robustness techniques including E-values, and sensitivity analyses to assess how hidden confounding could impact conclusions. By the end, you’ll have the skills to design, implement, and critically evaluate causal inference strategies under hidden confounding, ensuring that your insights are both credible and actionable.

Who is this book for?

This course is designed for data scientists, statisticians, applied researchers, and ML engineers who need to make causal claims from imperfect observational data. A working knowledge of Python and basic causal inference concepts is recommended. Professionals in healthcare, economics, policy, and business analytics will benefit from advanced tools to mitigate hidden confounding and ensure robust decision-making.