Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes

Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal. In addition, we implement the proposed inference methods to examine the effects of different medication regimens on long-term pain for patients experiencing motor vehicle collision.

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Multi-Cause Effect Estimation with Disentangled Confounder Representation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/384 ◽

2021 ◽

Author(s):

Jing Ma ◽

Ruocheng Guo ◽

Aidong Zhang ◽

Jundong Li

Keyword(s):

Real World ◽

Fundamental Problem ◽

Causal Effect ◽

Causal Effects ◽

Treatment Effect Estimation ◽

Latent Representations ◽

Real World Datasets ◽

New Perspective ◽

Multiple Treatments ◽

Effect Estimation

One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e.g., medicines in the prescription) on an important outcome (e.g., cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.

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Multiple imputation procedures for estimating causal effects with multiple treatments with application to the comparison of healthcare providers

Statistics in Medicine ◽

10.1002/sim.9231 ◽

2021 ◽

Author(s):

Gabriella C. Silva ◽

Roee Gutman

Keyword(s):

Multiple Imputation ◽

Healthcare Providers ◽

Causal Effects ◽

Multiple Treatments

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Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas

Statistical Science ◽

10.1214/17-sts612 ◽

2017 ◽

Vol 32 (3) ◽

pp. 432-454 ◽

Cited By ~ 38

Author(s):

Michael J. Lopez ◽

Roee Gutman

Keyword(s):

Causal Effects ◽

New Ideas ◽

Multiple Treatments

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Percutaneous sclerotherapy for spongiform venous malformations – analysis of patient-evaluated outcome and satisfaction

VASA ◽

10.1024/0301-1526/a000650 ◽

2017 ◽

Vol 46 (6) ◽

pp. 477-483

Author(s):

Robert Karl Clemens ◽

Frederic Baumann ◽

Marc Husmann ◽

Thomas Oleg Meier ◽

Christoph Thalhammer ◽

...

Keyword(s):

Retrospective Study ◽

Medical Records ◽

Venous Malformation ◽

Primary Treatment ◽

Venous Malformations ◽

Treated Area ◽

Item Questionnaire ◽

Percutaneous Sclerotherapy ◽

Multiple Treatments ◽

Pain Improvement

Abstract. Background: Congenital venous malformations are frequently treated with sclerotherapy. Primary treatment goal is to control the often size-related symptoms. Functional impairment and aesthetical aspects as well as satisfaction have rarely been evaluated. Patients and methods: Medical records of patients who underwent sclerotherapy of spongiform venous malformations were reviewed and included in this retrospective study. The outcome of sclerotherapy as self-reported by patients was assessed in a 21 item questionnaire. Results: Questionnaires were sent to 166 patients with a total of 327 procedures. Seventy-seven patients (48 %) with a total of 159 procedures (50 %) responded to the survey. Fifty-seven percent of patients were male. The age ranged from 1 to 38.1 years with a median age of 16.4 years. The lower extremities were the most common treated area. Limitations caused by the venous malformation improved in the majority of patients (e.g. pain improvement 87 %, improvement of swelling 83 %) but also worsening of symptoms occurred in a minority of cases. Seventy-seven per cent would undergo sclerotherapy again. Conclusions: Sclerotherapy for treatment of venous malformations results in significant reduction of symptoms. Multiple treatments are often needed, but patients are willing to undergo them.

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