Hi-CI: Deep Causal Inference in High Dimensions

Ankit Sharma, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, Lovekesh Vig, Gautam Shroff
Proceedings of the 2020 KDD Workshop on Causal Discovery, PMLR 127:39-61, 2020.

Abstract

We address the problem of counterfactual regression using causal inference (CI) in obser- vational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co- variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representa- tion in lower dimensions as compared to the original covariates, and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v127-sharma20a, title = {Hi-CI: Deep Causal Inference in High Dimensions}, author = {Sharma, Ankit and Gupta, Garima and Prasad, Ranjitha and Chatterjee, Arnab and Vig, Lovekesh and Shroff, Gautam}, booktitle = {Proceedings of the 2020 KDD Workshop on Causal Discovery}, pages = {39--61}, year = {2020}, editor = {}, volume = {127}, series = {Proceedings of Machine Learning Research}, month = {24 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v127/sharma20a/sharma20a.pdf}, url = {https://proceedings.mlr.press/v127/sharma20a.html}, abstract = {We address the problem of counterfactual regression using causal inference (CI) in obser- vational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co- variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representa- tion in lower dimensions as compared to the original covariates, and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.} }
Endnote
%0 Conference Paper %T Hi-CI: Deep Causal Inference in High Dimensions %A Ankit Sharma %A Garima Gupta %A Ranjitha Prasad %A Arnab Chatterjee %A Lovekesh Vig %A Gautam Shroff %B Proceedings of the 2020 KDD Workshop on Causal Discovery %C Proceedings of Machine Learning Research %D 2020 %E %F pmlr-v127-sharma20a %I PMLR %P 39--61 %U https://proceedings.mlr.press/v127/sharma20a.html %V 127 %X We address the problem of counterfactual regression using causal inference (CI) in obser- vational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co- variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representa- tion in lower dimensions as compared to the original covariates, and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.
APA
Sharma, A., Gupta, G., Prasad, R., Chatterjee, A., Vig, L. & Shroff, G.. (2020). Hi-CI: Deep Causal Inference in High Dimensions. Proceedings of the 2020 KDD Workshop on Causal Discovery, in Proceedings of Machine Learning Research 127:39-61 Available from https://proceedings.mlr.press/v127/sharma20a.html.

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