Causal inference course. Reduced form toolkit.

Causal inference course. Topics include randomized experiments, selection bias, regression adjustment, matching This course will focus on approaches to causal inference using the potential outcomes framework. The focus will be on formulating causal (research) questions, understanding sources of (avoidable and unavoidable) bias, as well as some basic methods. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. The first lesson introduces causal DAGs, a type of causal diagrams, and the rules that govern them. Oct 7, 2025 · Aims of Course This course aims to introduce the basic concepts of causal learning (reasoning, modelling, and inference); to enable you to read more advanced ‘causal’ papers. See curriculum info and more on this page. This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level. A must-have topics for data scientist and analysts, evidence-based decision-makers,. The previous section discusses causal assumptions at the population level. Please post questions in the YouTube co This is the online version of Causal Inference: The Mixtape Causal inference encompasses the tools that allow social scientists to determine what causes what. D. This course offers a rigorous mathematical survey of causal inference at the Master’s level. Additional information about the 2026 courses, including registration and course details, will be available in the coming weeks. Join today! We also train the next generation of investigators in causal inference via comprehensive education programs including online resources, seminar series and in-person courses at Harvard T. Learn key concepts in epidemiology, statistics, and health economics from experts in a range of sectors, including academia, regulatory, and industry. Nov 28, 2023 · Welcome Cornell STSCI / INFO / ILRST 3900. A course on causal inference would help a Statistician understand how to design studies, collect data, and analyze results in order to make valid conclusions about the relationships between variables. Express assumptions with causal graphs 4. Chan School of Public Health. Causal inference is essential to science, as we often want to make causal claims, rather than merely associational claims. Oct 4, 2024 · 16-lecture course on causal inference, the statistical science of drawing causal conclusions from experimental and non-experimental data. In contrast to a typical immersive in-person workshop, training, or boot camp, this course is designed for at-your-own-pace online learning, with short digestible course modules and lectures, but with enough depth to get full level mastery of the field. This textbook, based on the author’s course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability This course provides a hands-on introduction to statistical methods for causal inference. This course covers modern econometric techniques for evaluating causal effects based on observational (that is, non-experimental) data. Randomized designs and alternative designs and methods for when randomization is infeasible: matching methods, propensity scores, longitudinal treatments, regression discontinuity, instrumental variables, and principal stratification. Fall 2025. Implement several types of causal inference methods (e. In contrast, a causally explicit approach A Pearlian Approach to Causal Inference with Linear Regression COURSE DESCRIPTION Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Course provides students with a basic knowledge of both how to perform analyses and critique the use of some more advanced statistical methods useful in answering policy questions. In particular, using the “potential outcomes” framework of causality, we will focus on understanding which assumptions are necessary for giving The course will cover state-of-the-art research on causal reasoning and prepare students to conduct research in this area. Causal Inference with R is the first course in a series on causal inference concepts and methods created by Duke University with support from eBay, Inc. How are these concepts brought to data? I introduce a toolkit for causal inference in observational data that requires relatively few assumptions about the data generating process{speci cally, it involves assumptions about treatment assignment and adherence. Since half of the students were undergraduates, my lecture notes only required basic knowledge of probability theory, statistical inference, and linear and logistic regressions. Read about us here! Our introduction to causal inference course for health and social scientists, led by Dr Peter Tennant, offers a friendly and accessible training in the theory and practice of estimating causal effects in observational data. The course also presents methods for estimating causal effects in observational studies, for example, using historical data to estimate the impact of treatments that were introduced in the past. Available online. The class is targeted at PhD students who have already completed first-year coursework on the theory of statistics. , explores causal inference concepts & methods in multilevel settings. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. Oct 9, 2025 · In this course, you will learn how to uncover cause-and-effect relationships in observational data—an essential skill for driving business decisions, policy evaluations, and scientific insights. To learn more about Causal Inference and Target Trial Emulation click here. Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. Methods covered include randomized evaluations, instrumental variables, regression discontinuity, and At the end of the course, learners should be able to: 1. May 30, 2023 · I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. He explains the Rubin-Neyman causal model as a potential outcome framework. Organizations can harness the power of causal inference through randomized field experiments, uncovering the true effects of interventions and enabling data-driven decision-making. Sontag discusses causal inference, examples of causal questions, and how these guide treatment decisions. This We give you a taste of what we'll cover in the first few weeks of the Introduction to Causal Inference online course. 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 This repository serves as a curated collection of notes and textbooks related to causal inference, a fundamental area in statistical and econometric research. Chapter 3 of A first course in causal inference. As such, one might expect that any discussion of causal inference would need to be framed in terms of subtle and esoteric concepts. The first A First Course in Causal Inference The past decade has witnessed an explosion of interest in research and education in causal infer-ence, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. Methods are motivated by examples from social sciences, policy and health sciences STATS361 Course | Stanford University BulletinThis course covers statistical underpinnings of causal inference, with a focus on experimental design and data-driven decision making. Causal inference is a critical framework used to understand cause-and-effect relationships between variables, going beyond simple correlations to determine if changes in one variable directly cause changes in another. You’ll explore a powerful suite of causal inference methods designed for time-series and panel data, including Interrupted Time Series, Difference-in-Differences, Event Study, Synthetic Control This course offers a rigorous introduction to the theory and practice of causal inference, with emphasis on real-world applications. Course will provide an advanced treatment of how social science approaches can be used to inform policy, business and service delivery decisions. It was quite diferent even ten years ago when I was a Ph. This 2-day, in-person course introduces the target trial framework to assess comparative effectiveness and safety using healthcare databases. Check these in-person courses in Boston (CAUSALab), Madrid (CEMFI), Barcelona (RTI), and Wengen (University of Bern), as well as my online course at HarvardX. Content includes panel data analysis, evaluation methods, use of machine learning in social sciences, prediction, use of administrative data, text as Content of the course Causal inference from observational data is a key task of epidemiology and of allied sciences such as sociology, education, behavioral sciences, demography, economics, health services research, etc. The following UC Berkeley courses are taught by members of CTML on topics revelant to biostatistics, causal inference, and targeted learning. In particular, large randomized experiments let us recover the average treatment e ect (ATE) = E [Yi(1) Yi(0)] : (1. Causal Inference In health research, the questions that motivate most studies are interested in identifying causal relationships, not associational relationships. But what does this mean? In this course we will focus on empirical tools that economists use to identify causal relationships. Useful quote by George Barnard2: “in statistical inference, as distinct from mathematical inference, there is a world of difference between the two statements ”X is true” and ”X is known to be true”. Reduced form toolkit. The goal is to provide resources for learning, reference, and deeper understanding of causal modeling and inference techniques. Also includes examples of the practical side of causal inference and how to analyze real data with these methods. The presentation is mathematically precise, but accessible to students of various disciplines of health sciences with some previous background in biostatistical and epidemiological methods as specified above. It plays a crucial role in fields like medicine, economics, and social sciences, where understanding the impact of interventions or policies is essential. 1 Causal inference: models and inference ↔ reality. All four courses are open to the public and will take place in Boston, Massachusetts at the Harvard T. 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 See full list on bradyneal. . Statistical issues in causality and methods for estimating causal effects. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. Instructions for how to enroll can be found on each course's page. Course description Causal inference from observational data is a key task of epidemiology and of allied disciplines such as behavioural sciences and health services research. Jul 25, 2025 · An online self-paced course for quantitative researchers working with biomedical and social data, to introduce and develop core skills and confidence in addressing Causal Questions using Real World Data. This toolkit is called the reduced form Free Online Causal Inference Courses and Certifications Master statistical methods for establishing cause-and-effect relationships using R, Python, and experimental design techniques. Robins and Miguel A. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood The causal inference material relates to fields including social sciences and public health. Now, although i itself is fundamentally unknowable, we can (perhaps remarkably) use randomized experiments to learn certain properties of the i. At that time, causal inference was not a mainstream research Mar 25, 2025 · This live event has passed. Machine Learning & Causal Inference: A Short Course This course is a series of videos designed for any audience looking to learn more about how machine learning can be used to measure the effects of interventions, understand the heterogeneous impact of interventions, and design targeted treatment assignment policies. Samuel Wang, Mayleen Cortez-Rodriguez, and Daniel Molitor. This course covers the latest developments in causal inference methods and gives practical explanations about applying these methods to real research questions. The first part of this course is comprised of five lessons that introduce the theory of causal diagrams and describe its applications to causal inference. ” STATS 361 (also previously offered as OIT 661) is a graduate level class in causal inference, with a focus on topics including randomized and observational studies, doubly robust estimation, instrumental variables, graphical modeling, dynamic policies, etc. Feb 24, 2025 · Causal diagrams, in the form of directed acyclic graphs (DAGs), are presented as a visual representation of a postulated underlying causal structure, together with the key properties and rules used to determine the likely existence of confounding, selection and other biases that prevent unbiased estimation of causal effects. Check out these free resources: "Causal Inference: What If" book, HarvardX online course "Causal diagrams: Draw your assumptions before your conclusions", and CAUSALab software. Commonly used statistical methods estimate association measures which cannot always be causally interpreted, even when all potential confounders are included in the analysis. In particular, we will study how and when empirical research can make causal claims. While randomized experiments will be discussed, the primary focus will be the challenge of answering causal questions using data that do not meet such standards. Students will explore the concept of causality and estimation of counterfactuals using randomized and natural experiments to study economic and Machine learning: data → prediction. Define causal effects using potential outcomes 2. Apr 1, 2025 · Drawing reliable causal conclusions remains one of the biggest challenges in data science, where confounding variables and selection bias can lead to incorrect interpretations. 6 (83 ratings) 385 students The reading for the second lecture is Chapter 2 of A first course in causal inference. Hernán. Topics include randomization, potential outcomes, observational studies, propensity score methods, matching, double robustness, semiparametric efficiency, treatment heterogeneity, structural models, instrumental Aug 25, 2025 · I highly recommend to read Peng Ding's textbook [A first course in causal inference], which follows a similar structure as the course, but with more contents, details, proofs, and importantly, code and data. A draft textbook . He uses health data and causal inference methods to learn what works. Students will learn to estimate causal effects using both experimental and observational data, drawing on the potential outcomes framework and modern identification strategies. Join the first online course covering foundations of cause and effect studies. student in statistics. Causal Inference. g. The fifth lesson provides a simple graphical description of the bias of conventional statistical methods for confounding adjustment in the presence of time-varying covariates. The goal of the course is to expose graduate students to state-of-the-art research on causal inference. In this course, Experimental Design and Causal Inference in R, you'll gain the ability to move beyond correlation and establish true causal relationships in your data. Martin Spindler University of Hamburg On successful completion of this course, you should be able to: explain the difference between causal and associational estimation and justify why causal inference techniques are necessary to derive meaning from observational data explain the difference between randomised trials vs observational studies related to public health and other types of data more generally learn and apply Causal Inference Courses The following is a list of free courses in Causal Inference, sorted by format and date. Jun 27, 2025 · These understandings and tools come from the rapidly developing science of causal inference. Course objectives This is a seminar course. Students will put methods into practice using the statistical software R throughout the course. Applied Causal Inference Course This course offers a comprehensive overview of applied causal inference, focusing on developing a deep understanding of how to analyze and model cause-and-effect relationships in various domains. Get to know the modern tools for causal inference from machine learning and AI, with many practical examples in R Description This course is an introduction to modern causal inference theory and methods applicable to clinical and epidemiological research. Playlist of the causal inference course lectures, where each video is a full lecture, rather than each video as a chunk of a lecture like in the other playlist. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in Miguel Hernan is Director of CAUSALab and Professor of Epidemiology and Biostatistics at Harvard. However, a ground-breaking line of work starting with Neyman [1923] and Rubin [1974] established that|although causality is in general a Causal inference research and education in the past decade The past decade has witnessed an explosion of interest in research and edu-cation in causal inference, due to its wide applications in biomedical research, social sciences, tech companies, etc. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. Jun 3, 2024 · The CAUSALab will be hosting its annual summer of courses on causal inference between June 3 and June 14, 2024. For example, if we are choosing between treatments for a disease, we want to choose the treatment that causes the most people to be cured, without causing too many bad side effects. Prof. Samuel Wang, Filippo Fiocchi, and Shira Mingelgrin. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. 2) Causal Inference: Methods & Applications This course offers an in-depth exploration of causal inference, focusing on the methodologies used to establish and analyze causal relationships across various disciplines such as social sciences, health, and economics. Playlist of the causal inference course lectures, where each video is a conceptual chunk of a lecture. Welcome! Together, we will learn to reason about and assess the plausibility of causal claims by combining data with assumptions. Modern causal inference often tries to make minimal assumptions about the data and avoid relying on specific statistical models (“all models are wrong, but some are useful”). Intended as a continuation of API-209, Advanced Quantitative Methods I, this course focuses on developing the theoretical basis and practical application of the most common tools of empirical research. Taught by Christina Lee Yu, Y. Welcome! Together, we will learn to make causal claims by combining data with arguments. Summary With the ongoing “data explosion”, methods to delineate causation from correlation are perhaps more pressing now than ever. Many fields, from clinical medicine to social science, seek to use empirical data to learn how different factors affect the world. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. Several approaches for observational data including This course offers a rigorous mathematical survey of causal inference at the Master’s level. Enroll for free, earn a certificate, and build job-ready skills on your schedule. Fall 2023. These will include g-methods, propensity scores, and causal Explore causality and learn to infer causal effects from observational data in this crash course offered by the University of Pennsylvania. We will also cover various methodological tools including randomized experiments, regression discontinuity designs, matching, regression, instrumental variables, difference-in-differences, and dynamic causal models. We will be concerned with understanding how and when it is possible to make causal claims in empirical research. Taught by Ian Lundberg, Y. Find out more about these courses and how to apply. This course offers a rigorous mathematical survey of causal inference at the Master’s level. It will also use causal diagrams at an intuitive level. The course will draw upon examples from political science, economics, education, public health, and other disciplines. It also presents perspectives and tools to help you formalize and conceptualize causal relationships. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this introduction focuses on how to interpret treatment effects, and how to explore and derive key summary statistics from dataframes. [1][2] The study of why things occur is called etiology, and Course Description This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. This course will introduce the Causal Roadmap, which is a general framework for Causal Inference: (1) clear statement of the research question, (2) definition of the causal model and effect of interest, (3) specification of the observed data, (4) assessment of Miguel Hernan teaches methods for causal inference to researchers who generate or repurpose data to support decision-making. correlation, causal inference, causal identification and counterfactual. The second, third, and fourth lessons use causal DAGs to represent common forms of bias. Causal inference goes beyond prediction by modeling the outcome of interventions and formal-izing counterfactual reasoning. In this course, we will delve into how companies are currently utilizing A/B testing. Course overview: Develop your skills in pharmacoepidemiology, pharmacovigilance, and real-world evidence with our intensive online short courses. Causality in machine learning by Robert Ness [Lecture Notes] [Github] Causal inference and learning by Elena Zheleva [Reading list] Causal data science by Paul Hunermund [Course] And here are some sets of lectures. Delivered in-person over five-days by a team of experts, the course provides an unmissable introduction to this exciting and evolving aspect of contemporary data science. Read about us here! Description: This course is designed to introduce students to basic of causal inference, including potential outcomes, counterfactuals, confounding, mediation, and instrumental variables. Courses There are a few good courses to get started on causal inference and their applications in computing/ML systems. Causal inference is the process of determining whether and how one variable influences another, going beyond simple correlations and attempting to uncover cause-and-effect relationships. On the conceptual level, the course covers basic concepts such as causation vs. Such problems arise in many business applications including in finance, policymaking, and health care. Inferences about causation are of great importance in science, medicine, policy, and business. List of Causal Inference Courses Offered by StanfordWeb Login A online workshop in causal modeling and causal inference in a machine learning context. It plays a crucial role across fields such as statistics, data science, machine learning, healthcare, social sciences, and empirical research broadly… Sep 11, 2025 · Course Description This course provides an introduction to statistical methods used for causal inference in the social sciences. Speaker: David Sontag Lecture 14: Causal Inference, Part 1 slides (PDF - 2. Jun 11, 2025 · The first part of this course is comprised of seven lessons that introduce causal diagrams and its applications to causal inference. Dr. The readings for the third lecture are Section 2. com 5 days ago · Welcome Cornell STSCI / INFO / ILRST 3900. Dec 9, 2024 · Causal Inference “Correlation is not causation” is a frequent refrain from people investigating relationships in data. 1 of the textbook Causal inference: what if by James M. Unlike many other causal inference textbooks that are written for applied researchers or econometricians, this book is (to my mind) clearly written with a statistical audience in mind. The objectives of this course will be to understand the design of observational epidemiological studies, comprehend the principles of causality, and to know the epidemiological and analytical methods to make causal inference from observational studies. Applications are drawn from a variety of fields including political science, economics Transform you career with Coursera's online Causal Inference courses. matching, instrumental variables, inverse probability of treatment weighting) 5. Jun 27, 2025 · "A first course in causal inference" by Peng Ding is a welcome addition to the textbooks available to teach an introductory course on causal inference. Jun 7, 2024 · This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. We will discuss causal inference under interference later in the course. Unlike traditional statistical analysis, causal Chapter 1 Randomized Controlled Trials How best to understand and characterize causality is an age-old question in philosophy. These disciplines share a methodological framework for causal inference that has been developed over the last decades. This course will cover state-of-the-art social science techniques using a range of data sources. Describe the difference between association and causation 3. Video Lectures Slides Notes Looking for a course in causal inference/book which doesn’t shy away from heavy notation and theory, and is foundational. 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 Jan 19, 2023 · Course Catalogue - Methods for Causal Inference (INFR11207) Causal inference under the potential outcome framework is essentially a missing data problem To identify causal effects from observed data, one must make additional (structural or/and stochastic) assumptions Key identifying assumptions are on assignment mechanism: the probabilistic rule that decides which unit gets assigned to which treatment Causal AI: An Introduction Learn the foundational components of Causal Artificial Intelligence 4. H. Chan School of Public Health from 9:30 am to 4:30 pm (ET) every day. For example, what is the efficacy of a specific drug on a specific population? What was the cause of death of a given individual? Are certain environmental exposures harmful? What is the efficacy of new therapy X? Causal inference is one of the most important and challenging aims in statistical and data science. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data. NEW Course! Causal Inference: Methods and Applications August 6-7, 2025 2 days, 8:30 AM – 4:30 PM The Conference Board New York, NY This course offers an in-depth exploration of causal inference, focusing on the methodologies used to establish and analyze causal relationships across various disciplines such as social sciences, health, and On successful completion of this course, you should be able to: explain the difference between causal and associational estimation and justify why causal inference techniques are necessary to derive meaning from observational data explain the difference between randomised trials vs observational studies related to public health and other types of data more generally learn and apply This online Distinguished Speaker workshop by Stephen Raudenbush, Ed. Introduction to Causal Inference Causal Inference and Digital Causality Lab Prof. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science The first part of this course is comprised of seven lessons that introduce causal diagrams and its applications to causal inference. Feb 21, 2025 · Discover the fundamentals of causal inference in our comprehensive course, covering statistical analysis, treatment effects, and confounding variables to help researchers make informed decisions and draw accurate conclusions from data analysis and machine learning techniques. 2MB) CAUSALab’s 2026 Summer Courses on Causal Inference will be held June 2026. vj nalm kwk yo9hdb dnjpo idosnn 6uxo zp lqn1 lc