Causal inference

The Regression discontinuity design in epidemiology

With [Sara Geneletti](http://www.lse.ac.uk/Statistics/People/Dr-Sara-Geneletti), [Aidan O'Keeffe](https://www.ucl.ac.uk/statistics/people/aidan-okeeffe), [Sylvia Richardson](https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/n-to-s/sylvia-richardson/), [Linda Sharples](https://www.lshtm.ac.uk/aboutus/people/sharples.linda), [Irwin Nazareth](https://iris.ucl.ac.uk/research/personal?upi=IDNAZ80), [Federico Ricciardi](https://www.ucl.ac.uk/statistics/people/federico-ricciardi) and Mariam Adeleke

Population adjustment with limited access to patient-level data

[Antonio Remiro-Azocar](http://remiroazocar.com/), [Gianluca Baio](www.statistica.it/gianluca) and [Anna Heath](https://sites.google.com/site/annaheathstats/)

A Bayesian hierarchical framework to evaluate policy effects through quasi-experimental designs

With [Marta Blangiardo](http://www.statistica.it/marta), [James Kirkbride](https://iris.ucl.ac.uk/iris/browse/profile?upi=JBKIR68), [Sara Geneletti](http://www.lse.ac.uk/Statistics/People/Dr-Sara-Geneletti) and Zejing Shao

Bayesian Tree-Based Learners for Individualized Treatment Effects Estimation

With [Alberto Caron](https://sites.google.com/view/albertocaron/) and [Ioanna Manolopoulou](http://www.homepages.ucl.ac.uk/~ucakima/)

Personalised seminars

Marcos Vera Hernandez (who’s one of the co-director of our MSc programme) and his colleague Toru Kitagawa have been involved in the organisation of a couple of very interesting seminars on the econometrics of personalised medicine at CeMMAP/UCL. The first one is a masterclass by Charles Manski of Northwestern University in the US. His talk will be a two-day events on the 28th-29th March. Here’s a link to register to the masterclass.

Bayesian modelling for binary outcomes in the Regression Discontinuity Design

Summary The regression discontinuity (RD) design is a quasi-experimental design which emulates a randomized study by exploiting situations where treatment is assigned according to a continuous variable as is common in many drug treatment guidelines. …

Brexit^{-1}

I’ve been asked to post about the EuroCIM (European Causal Inference Meeting), which will be held later this year in Florence. I very happily oblige, because: a) this is usually a very good conference; b) it is organised by nice and obviously very good people (well $-$ at least I like them!); c) at a time where everything UK seem to move away from anything Euro, it’s actually very nice to see a conference formerly known as UKCIM going fully Euro!

Workshop on The Regression Discontinuity Design

As part of our bid to get an MRC grant (which we managed to do), we promised that, if successful, we’d also have a dissemination workshop, at the end of the project. Well, the project on the Regression Discontinuity Design (RDD) has now finished for a few months, but we’re keeping our word and we have actually organised something that, as it happens, has probably turned into something slightly bigger (and better!

Face value

This is actually a not-so-recent paper, but I’ve only discovered now and I think it’s very interesting. The underlying issue is about trying to do “causal inference” from observational data $-$ perhaps one could see this in a simpler way by considering the idea of “balancing” observational data, to mimic as far as possible an experimental setting (and so be able to estimate “causal” effects). [There’s lot more on the philosophical aspects behind this problem, which I’m conveniently swiping under the carpet, here…] _ _Anyway, one of the most popular ways of dealing with this issue of unbalanced background covariates (or generally, confounding) is to use propensity score matching.

PhD opportunity!

Applications are invited for a PhD funding opportunity to conduct research in a branch of probability or statistics based in the UCL Department of Statistical Science, commencing in September 2017. This funding is provided by the Engineering and Physical Sciences Research Council (EPSRC). The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelor’s degree, or a Master’s degree with merit or distinction, in Mathematics, Statistics, Computer Science, or a related quantitative discipline.