--- title: Causal Inference & Experiments description: Causal inference is the process of determining the independent, actual effect of a particular phenomenon. You will understand how to design randomized experiments, control for confounding variables, and analyze observational data to establish cause-and-effect relationships. category: programming-tech subcategory: data-science difficulty: beginner, intermediate, advanced url: /subject/causal-inference-and-experiments --- # Causal Inference & Experiments Causal inference is the process of determining the independent, actual effect of a particular phenomenon. You will understand how to design randomized experiments, control for confounding variables, and analyze observational data to establish cause-and-effect relationships. ## Available Resources 2 Books • 1 Courses • 5 Websites ## Websites ### 1. Python Causality Handbook Matheus Facure's open online book, Causal Inference for the Brave and True, teaching potential outcomes, regression, instrumental variables, difference-in-differences, and matching through runnable Python notebooks. Readers finish able to estimate treatment effects from observational data. **Difficulty:** Beginner | **Price:** Free **Link:** https://matheusfacure.github.io/python-causality-handbook/ **Tags:** causal-inference, python, econometrics, treatment-effects, observational-data ### 2. Causal Inference Book (Hernán) Miguel Hernan and James Robins's textbook Causal Inference: What If, free as a PDF, developing counterfactuals, confounding, standardization, inverse probability weighting, instrumental variables, and g-methods, with accompanying code in R, Python, SAS, and Stata. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ **Tags:** causal-inference, epidemiology, counterfactuals, g-methods, textbook ### 3. causalinference.gitlab.io A GitLab Pages site with educational content on causal inference and experimental design, featuring tutorials, lecture notes, and reference materials on topics such as randomized experiments, observational causal methods, potential outcomes, and causal diagrams. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://causalinference.gitlab.io **Tags:** websites, technology-computer-science, data-science--ai ### 4. mixtape.scunning.com Mixtape is a teaching resource that compiles concise notes, tutorials, datasets, and runnable code for causal inference and experimental design. It offers practical, hands-on material on randomized trials, quasi-experiments, and evaluation methods with example analyses. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://mixtape.scunning.com **Tags:** websites, technology-computer-science, data-science--ai ### 5. povertyactionlab.org J-PAL Poverty Action Lab is a global research center that uses randomized controlled trials to evaluate anti-poverty programs. The site provides research results, policy briefs, case studies, and teaching resources on experimental design, data collection, and impact evaluation. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://povertyactionlab.org **Tags:** websites, technology-computer-science, data-science--ai ## Podcasts ### 1. Causal Bandits **Author:** Alex Molak Causal Bandits Podcast with Alex Molak is here to help you learn about causality, causal AI and causal machine learning through the genius of others. The podcast focuses on causality from a number of different perspectives, finding common grounds between academia and industry, philosophy, theory and practice, and between different schools of thought, and traditions. Your host, Alex Molak is an a machine learning engineer, best-selling author, and an educator who decided to travel the world to record conversations with the most interesting minds in causality to share them with you.Enjoy and stay causal!Keywords: Causal AI, Causal Machine Le... **Difficulty:** Beginner | **Language:** en-us | **Price:** Free **Link:** https://causalbanditspodcast.com **Tags:** causal-inference, causal-ai, machine-learning, interviews ## Books ### 1. The Book of Why **Author:** Judea Pearl, Dana Mackenzie Pearl's popular account of the causal revolution in statistics, introducing the ladder of causation, causal diagrams, and do-calculus. Readers come away understanding why correlation-based methods cannot answer intervention and counterfactual questions. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1541698967?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai ### 2. Mostly Harmless Econometrics **Author:** Joshua D. Angrist, Jorn-Steffen Pischke An applied econometrics text built around research design: randomized trials, regression, instrumental variables, differences-in-differences, and regression discontinuity. Readers finish able to judge whether an empirical study's identification strategy actually supports its causal claims. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0691120358?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai ## Courses ### 1. Causal Inference This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course. **Difficulty:** Beginner | **Language:** English | **Duration:** 6 weeks of study, 3-5 hours per week | **Price:** Free **Link:** https://www.coursera.org/learn/causal-inference **Tags:** courses, technology-computer-science, data-science--ai --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/causal-inference-and-experiments