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TRAINING

DATA SCIENCE ADVANCED COURSES

Estimating Causal Effects
7 – 9 MAY 2024

Bianca De Stavola, Rhian Daniel, and Andrea Aparicio Castro

This course will introduce participants to the two main approaches to estimating causal effects from observational data: those based on the assumption of no unmeasured confounding and those that exploit the availability of instrumental variables. The course will focus on settings where the exposure/intervention is time fixed but will also give an introduction to the more general case when exposures/treatments are time-varying (and hence may be affected by time varying confounding).

To develop an understanding of the assumptions most commonly invoked in causal inference and some practical expertise in applying estimation methods that rely on them.

There will be 5 on-line live lectures sessions and 3 on-line live discussion/computer practical sessions.

Date

Time

7-9/05/2024

10:00 to 13:00

It I advisable that, before attending this course, participants attend both the module called “Addressing Causal Questions using real work data: an Introduction” and the short course called “Introduction to Causal Diagrams”, or equivalently be familiar  with the first 2 parts of the book by  Hernán and Robins “ Causal Inference: What If.” which is available here:

https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2022/11/hernanrobins_WhatIf_13nov22.pdf

£45 for PhD Students and £90 for others