ADCB: An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects

Newton Mwai Kinyanjui, Fredrik D Johansson
Proceedings of the Conference on Health, Inference, and Learning, PMLR 174:103-118, 2022.

Abstract

Simulators make unique benchmarks for causal effect estimation as they do not rely on unverifiable assumptions or the ability to intervene on real-world systems. This is especially important for estimators targeting healthcare applications as possibilities for experimentation are limited with good reason. We develop a simulator of clinical variables associated with Alzheimer’s disease, aimed to serve as a benchmark for causal effect estimation while modeling intricacies of healthcare data. We fit the system to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and ground hand-crafted components in results from comparative treatment trials and observational treatment patterns. The simulator includes parameters which alter the nature and difficulty of the causal inference tasks, such as latent variables, effect heterogeneity, length of observed subject history, behavior policy and sample size. We use the simulator to compare standard estimators of average and conditional treatment effects.

Cite this Paper

BibTeX
@InProceedings{pmlr-v174-kinyanjui22a, title = {ADCB: An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects}, author = {Kinyanjui, Newton Mwai and Johansson, Fredrik D}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {103--118}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/kinyanjui22a/kinyanjui22a.pdf}, url = {https://proceedings.mlr.press/v174/kinyanjui22a.html}, abstract = {Simulators make unique benchmarks for causal effect estimation as they do not rely on unverifiable assumptions or the ability to intervene on real-world systems. This is especially important for estimators targeting healthcare applications as possibilities for experimentation are limited with good reason. We develop a simulator of clinical variables associated with Alzheimer’s disease, aimed to serve as a benchmark for causal effect estimation while modeling intricacies of healthcare data. We fit the system to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and ground hand-crafted components in results from comparative treatment trials and observational treatment patterns. The simulator includes parameters which alter the nature and difficulty of the causal inference tasks, such as latent variables, effect heterogeneity, length of observed subject history, behavior policy and sample size. We use the simulator to compare standard estimators of average and conditional treatment effects.} }
Endnote
%0 Conference Paper %T ADCB: An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects %A Newton Mwai Kinyanjui %A Fredrik D Johansson %B Proceedings of the Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2022 %E Gerardo Flores %E George H Chen %E Tom Pollard %E Joyce C Ho %E Tristan Naumann %F pmlr-v174-kinyanjui22a %I PMLR %P 103--118 %U https://proceedings.mlr.press/v174/kinyanjui22a.html %V 174 %X Simulators make unique benchmarks for causal effect estimation as they do not rely on unverifiable assumptions or the ability to intervene on real-world systems. This is especially important for estimators targeting healthcare applications as possibilities for experimentation are limited with good reason. We develop a simulator of clinical variables associated with Alzheimer’s disease, aimed to serve as a benchmark for causal effect estimation while modeling intricacies of healthcare data. We fit the system to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and ground hand-crafted components in results from comparative treatment trials and observational treatment patterns. The simulator includes parameters which alter the nature and difficulty of the causal inference tasks, such as latent variables, effect heterogeneity, length of observed subject history, behavior policy and sample size. We use the simulator to compare standard estimators of average and conditional treatment effects.
APA
Kinyanjui, N.M. & Johansson, F.D.. (2022). ADCB: An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects. Proceedings of the Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 174:103-118 Available from https://proceedings.mlr.press/v174/kinyanjui22a.html.

Related Material