BSU Seminar: "IV-learner: learning conditional average treatment effects using instrumental variables"

Add to your list(s) Download to your calendar using vCal

• Karla Diaz Ordaz, University College London
• Tuesday 19 November 2024, 14:00-15:00
• MRC Biostatistics Unit, East Forvie Building, Forvie Site Robinson Way Cambridge CB2 0SR..

If you have a question about this talk, please contact Alison Quenault.

This will be a free hybrid seminar. To register to attend virtually, please click here: https://cam-ac-uk.zoom.us/meeting/register/tZ0od-CorDouHdEyadWd2HkaL_HUObpPwuoJ

Instrumental variable methods are very popular in econometrics and biostatistics for inferring causal average effects of an exposure on an outcome where there is unmeasured confounding. However, their application for learning heterogeneous treatment effects, such as conditional average treatment effects (CATE), in combination with machine learning, is somewhat limited.

A generic approach that allows the use of arbitrary machine learning algorithms can be based on the popular two-stage principle. We first “regress” the exposure on the instrumental variables (and pre-exposure covariates) and then learn the causal treatment effects by regressing the outcome on the predicted exposure. This is the approach of Foster and Syrgkanis (2023), referred to as IV-debiased machine learning (IV-DML). Unfortunately, the slow convergence rates of the data-adaptive estimators that affect the first-stage predictions propagate into the resulting CATE estimates.

In view of this, we propose the IV-learner, inspired by infinite-dimensional targeted learning procedures (Vansteelandt 2023, van der Laan et al 2024). It strategically targets the first-stage predictions so they perform well in their ultimate task: CATE estimation. The resulting learner is easy to construct based on arbitrary, off-the-shelf algorithms.

We study the finite sample performance of our proposal using simulations, and compare it to existing methods. We also illustrate it using a real data example.

This talk is part of the MRC Biostatistics Unit Seminars series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Immunology
• Cambridge talks
• Cardiovascular Epidemiology Unit Special Seminars
• Chris Davis' list
• Department of Public Health and Primary Care
• Graduate-Seminars
• Interested Talks
• Life Sciences
• Life Sciences
• ME Seminar
• MRC Biostatistics Unit Seminars
• MRC Biostatistics Unit, East Forvie Building, Forvie Site Robinson Way Cambridge CB2 0SR.
• Primary Care
• PublicHealth@Cambridge
• Trust & Technology Initiative - interesting events
• bld31
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• primary care
• rp587

Note that ex-directory lists are not shown.