Causal inference under hidden confounding.

Format:

Video

Contributor:

Molak, Aleksander, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Python (Computer program language).

Causation.

Inference.

Physical Description:

1 online resource (1 video file (49 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2025.

Summary:

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 na©¯ve 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. To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub. What you will learn Represent causal assumptions with DAGs under confounding Use front-door and proximal methods for identification Leverage negative controls to detect hidden confounding Apply causal inference techniques to real-world datasets Critically assess robustness of causal conclusions Audience 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. About the Author Aleksander Molak: Founder of CausalPython.io, host of the Causal Bandits Podcast, and Senior Data Science Consultant at Lingaro. Also serves as a Causal Machine Learning Tutor at the University of Oxford.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

1-80638-631-3

OCLC:

1550494205