Causal Analysis in Theory and Practice

Hello friends in causality research!

UCLA is proud to host the 2017 West Coast Experiments Conference. See the details below for more information:

West Coast Experiments Conference: The WCE is an annual conference that brings together leading scholars and graduate students in economics, political science and other social sciences who share an interest in causal identification broadly speaking. Now in its tenth year, the WCE is a venue for methodological instruction and debate over design-based and observational methods for causal inference, both theory and applications.

Speakers: Judea Pearl, Rosa Matzkin, Niall Cardin, Angus Deaton, Chris Auld, Jeff Wooldridge, Ed Leamer, Karim Chalak, Rodrigo Pinto, Clark Glymour, Elias Barenboim, Adam Glynn, and Karthika Mohan.

Dates/Location: The tenth annual West Coast Experiments Conference will be held at UCLA on Monday, April 24 and Tuesday, April 25, 2017, preceded by in-depth methods training workshops on Sunday, April 23. Events will be held in the Covel Commons Grand Horizon Ballroom, 200 De Neve Drive, Los Angeles, CA 90095.

Fees: Attendance is free!

Registration and details: Space is limited; for a detailed schedule of events and registration, please visit: wce2017ucla.eventbrite.com

Greetings!

We would like to promote another causal inference short course. This 2-day seminar won the 2013 Causality in Statistics Education Award, given by the American Statistical Association. See the details below for more information:

Causal Inference with Directed Graphs: This seminar offers an applied introduction to directed acyclic graphs (DAGs) for causal inference. DAGs are a powerful new tool for understanding and resolving causal problems in empirical research. DAGs are useful for social and biomedical researchers, business and policy analysts who want to draw causal inferences from non-experimental data. The chief advantage of DAGs is that they are “algebra-free,” relying instead on intuitive yet rigorous graphical rules.

Instructor: Felix Elwert, Ph.D.

Who should attend: If you want to understand under what circumstances you can draw causal inferences from non-experimental data, this course is for you. Participants should have a good working knowledge of multiple regression and basic concepts of probability. Some prior exposure to causal inference (counterfactuals, propensity scores, instrumental variables analysis) will be helpful but is not essential.

Tuition: The fee of $995.00 includes all seminar materials.

Date/Location: The seminar meets Friday, April 28 and Saturday, April 29 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.

Details and registration: http://statisticalhorizons.com/seminars/public-seminars/causal-inference-with-directed-graphs-spring

Greetings!

We’ve received news that Harvard is offering a short course on causal inference that may be of interest to readers of this blog. See the details below for more information:

An Introduction to Causal Inference: This 5-day course introduces concepts and methods for causal inference from observational data. Upon completion of the course, participants will be prepared to further explore the causal inference literature. Topics covered include the g-formula, inverse probability weighting of marginal structural models, g-estimation of structural nested models, causal mediation analysis, and methods to handle unmeasured confounding. The last day will end with a “capstone” open Q&A session.

Instructors: Miguel Hernán, Judith Lok, James Robins, Eric Tchetgen Tchetgen & Tyler VanderWeele

Prerequisites: Participants are expected to be familiar with basic concepts in epidemiology and biostatistics, including linear and logistic regression and survival analysis techniques.

Tuition: $450/person, to be paid at the time of registration. Tuition will be waived for up to 2 students with primary affiliation at an institution in a developing country.

Date/Location: June 12-16, 2017 at the Harvard T.H. Chan School of Public Health

Details and registration: https://www.hsph.harvard.edu/causal/shortcourse/