TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective

12/17/2022
by   Pengfei Xi, et al.
4

Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/20/2021

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments

Estimating an individual's potential response to interventions from obse...
research
02/10/2020

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

Identifying when to give treatments to patients and how to select among ...
research
06/04/2022

Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios

Evaluation of intervention in a multi-agent system, e.g., when humans sh...
research
12/19/2019

Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation

Counterfactual reasoning is an important paradigm applicable in many fie...
research
03/23/2020

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

Counterfactual prediction is a fundamental task in decision-making. G-co...
research
07/19/2021

CETransformer: Casual Effect Estimation via Transformer Based Representation Learning

Treatment effect estimation, which refers to the estimation of causal ef...
research
09/01/2022

Switchback Experiments under Geometric Mixing

The switchback is an experimental design that measures treatment effects...

Please sign up or login with your details

Forgot password? Click here to reset