Image-based Treatment Effect Heterogeneity

Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud
Proceedings of the Second Conference on Causal Learning and Reasoning, PMLR 213:531-552, 2023.

Abstract

Randomized controlled trials (RCTs) are considered the gold standard for estimating the Average Treatment Effect (ATE) of interventions. One important use of RCTs is to study the causes of global poverty – a subject explicitly cited in the 2019 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel awarded to Duflo, Banerjee, and Kremer “for their experimental approach to alleviating global poverty.” Because the ATE is a population summary, researchers often want to better understand how the treatment effect varies across different populations by conditioning on tabular variables such as age and ethnicity that were measured during the RCT data collection. Although such variables carry substantive importance, they are often only observed only near the time of the experiment: exclusive use of such variables may fail to capture historical, geographical, or neighborhood-specific contributors to effect variation. In global poverty research, when the geographical location of the experiment units is approximately known, satellite imagery can provide a window into such historical and geographical factors important for understanding heterogeneity. However, there is no causal inference method that specifically enables applied researchers to analyze Conditional Average Treatment Effects (CATEs) from images. In this paper, we develop a deep probabilistic modeling framework that identifies clusters of images with similar treatment effect distributions, enabling researchers to analyze treatment effect variation by image. Our interpretable image CATE model also emphasizes an image sensitivity factor that quantifies the importance of image segments in contributing to the mean effect cluster prediction. We compare the proposed methods against alternatives in simulation; additionally, we show how the model works in an actual RCT, estimating the effects of an anti-poverty intervention in northern Uganda and obtaining a posterior predictive distribution over treatment effects for the rest of the country where no experimental data was collected. We make code for all modeling strategies available in an open-source software package and discuss their applicability in other domains (such as the biomedical sciences) where image data are also prevalent.

Cite this Paper

BibTeX
@InProceedings{pmlr-v213-jerzak23a, title = {Image-based Treatment Effect Heterogeneity}, author = {Jerzak, Connor Thomas and Johansson, Fredrik Daniel and Daoud, Adel}, booktitle = {Proceedings of the Second Conference on Causal Learning and Reasoning}, pages = {531--552}, year = {2023}, editor = {van der Schaar, Mihaela and Zhang, Cheng and Janzing, Dominik}, volume = {213}, series = {Proceedings of Machine Learning Research}, month = {11--14 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v213/jerzak23a/jerzak23a.pdf}, url = {https://proceedings.mlr.press/v213/jerzak23a.html}, abstract = {Randomized controlled trials (RCTs) are considered the gold standard for estimating the Average Treatment Effect (ATE) of interventions. One important use of RCTs is to study the causes of global poverty – a subject explicitly cited in the 2019 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel awarded to Duflo, Banerjee, and Kremer “for their experimental approach to alleviating global poverty.” Because the ATE is a population summary, researchers often want to better understand how the treatment effect varies across different populations by conditioning on tabular variables such as age and ethnicity that were measured during the RCT data collection. Although such variables carry substantive importance, they are often only observed only near the time of the experiment: exclusive use of such variables may fail to capture historical, geographical, or neighborhood-specific contributors to effect variation. In global poverty research, when the geographical location of the experiment units is approximately known, satellite imagery can provide a window into such historical and geographical factors important for understanding heterogeneity. However, there is no causal inference method that specifically enables applied researchers to analyze Conditional Average Treatment Effects (CATEs) from images. In this paper, we develop a deep probabilistic modeling framework that identifies clusters of images with similar treatment effect distributions, enabling researchers to analyze treatment effect variation by image. Our interpretable image CATE model also emphasizes an image sensitivity factor that quantifies the importance of image segments in contributing to the mean effect cluster prediction. We compare the proposed methods against alternatives in simulation; additionally, we show how the model works in an actual RCT, estimating the effects of an anti-poverty intervention in northern Uganda and obtaining a posterior predictive distribution over treatment effects for the rest of the country where no experimental data was collected. We make code for all modeling strategies available in an open-source software package and discuss their applicability in other domains (such as the biomedical sciences) where image data are also prevalent.} }
Endnote
%0 Conference Paper %T Image-based Treatment Effect Heterogeneity %A Connor Thomas Jerzak %A Fredrik Daniel Johansson %A Adel Daoud %B Proceedings of the Second Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2023 %E Mihaela van der Schaar %E Cheng Zhang %E Dominik Janzing %F pmlr-v213-jerzak23a %I PMLR %P 531--552 %U https://proceedings.mlr.press/v213/jerzak23a.html %V 213 %X Randomized controlled trials (RCTs) are considered the gold standard for estimating the Average Treatment Effect (ATE) of interventions. One important use of RCTs is to study the causes of global poverty – a subject explicitly cited in the 2019 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel awarded to Duflo, Banerjee, and Kremer “for their experimental approach to alleviating global poverty.” Because the ATE is a population summary, researchers often want to better understand how the treatment effect varies across different populations by conditioning on tabular variables such as age and ethnicity that were measured during the RCT data collection. Although such variables carry substantive importance, they are often only observed only near the time of the experiment: exclusive use of such variables may fail to capture historical, geographical, or neighborhood-specific contributors to effect variation. In global poverty research, when the geographical location of the experiment units is approximately known, satellite imagery can provide a window into such historical and geographical factors important for understanding heterogeneity. However, there is no causal inference method that specifically enables applied researchers to analyze Conditional Average Treatment Effects (CATEs) from images. In this paper, we develop a deep probabilistic modeling framework that identifies clusters of images with similar treatment effect distributions, enabling researchers to analyze treatment effect variation by image. Our interpretable image CATE model also emphasizes an image sensitivity factor that quantifies the importance of image segments in contributing to the mean effect cluster prediction. We compare the proposed methods against alternatives in simulation; additionally, we show how the model works in an actual RCT, estimating the effects of an anti-poverty intervention in northern Uganda and obtaining a posterior predictive distribution over treatment effects for the rest of the country where no experimental data was collected. We make code for all modeling strategies available in an open-source software package and discuss their applicability in other domains (such as the biomedical sciences) where image data are also prevalent.
APA
Jerzak, C.T., Johansson, F.D. & Daoud, A.. (2023). Image-based Treatment Effect Heterogeneity. Proceedings of the Second Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 213:531-552 Available from https://proceedings.mlr.press/v213/jerzak23a.html.

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