Introduction to machine learning for causal analysis using observational data - Online (join a waiting list)
Date:
22/06/2021 - 23/06/2021
Organised by:
NCRM, University of Southampton
Presenter:
Professor Paul Clarke, Dr Spyros Samothrakis and Damian Machlanski
Level:
Intermediate (some prior knowledge)
Contact:
Jacqui Thorp
Training and Capacity Building Co-Ordinator
National Centre for Research Methods
University of Southampton
Email: jmh6@soton.ac.uk
Venue: Online
Description:
This two-day online workshop will
- Introduce the basic principles of causal modelling (potential outcomes, graphs, causal effects) while emphasising the key role of design and assumptions in obtaining robust estimates.
- Introduce the basic principles of machine learning and the use of machine learning methods to do causal inference (e.g. methods stemming from domain adaptation and propensity scores).
- Show how to implement these techniques for causal analysis and interpret the results in illustrative examples.
The course covers:
- Causal modelling
- Basic machine learning techniques
- Running simple causal analysis using machine learning on real data sets
By the end of the course participants will:
- Understand the distinction between causal effects and associations and appreciate the key role of design and possibly untestable assumptions in the estimation of causal effects
- Understand the role of training and testing models on data and the use of regularization to avoid overfitting
- Be able to position machine learning within the causal tool chain
This course is aimed at all quantitative researchers, academic and non-academic, with experience/knowledge of performing causal analysis with data from observational studies and of some of the challenges (e.g. adjusting for confounding bias/selection on observables, non-random selection, endogenous regressors). It should be suitable for junior researchers or senior researchers who wish to get a hands-on introduction to this topic.
Some prior knowledge of programming would be desirable but not essential. Experience with some statistical package should be sufficient to understand and run the exercises. Some familiarity with high-level programming concepts. Ideally, if you want to participate in the practical element of the course, have a python interpreter installed in your computer.
Preparatory Reading
Non required, but if you want to delve into the practical side of things, please do an online introductory python module to get some familiarity with the language.
See here for a list of tutorials:
https://docs.python-guide.org/intro/learning/
Duration
Two-day online course, starting 9:30 and finishing 16:30, with an one hour break for lunch at 12:00. The morning will be standard teaching/discussion, while the afternoon will be guided problem solving/programming.
Cost:
The fee per teaching day is:• £30 per day for UK/EU registered students• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff, staff at UK/EU registered charity organisations and recognised UK/EU research institutions. • £100 per day for all other participantsIn the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date.If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of the cancellation of a course.The University of Southampton’s Online Store T&Cs also continue to apply.
Website and registration:
Region:
South East
Keywords:
Explanatory Research and Causal analysis, Machine Learning , Causal Inference , Observational Data
Related publications and presentations:
Explanatory Research and Causal analysis