Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2002.04083v1)

Abstract: Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.

Overview of Estimating Counterfactual Treatment Outcomes Using Adversarially Balanced Representations

The paper introduces the Counterfactual Recurrent Network (CRN), a sequence-to-sequence model that improves the estimation of treatment effects over time using observational patient data. This approach addresses challenges in clinical decision-making, such as determining when to administer treatments and how to select among multiple options over time. The work incorporates adversarial training to generate balanced representations, overcoming biases introduced by time-varying confounders.

Problem Context

The primary challenge when estimating treatment effects in a longitudinal setting relates to time-dependent confounders. These confounders are patient covariates influenced by previous treatments and affecting subsequent treatment choices. The bias from these confounders must be alleviated to make accurate counterfactual predictions about potential treatment outcomes. Existing methodologies primarily address static settings and falter in longitudinal scenarios where confounders evolve over time.

Proposed Solution: Counterfactual Recurrent Network

The CRN employs domain adversarial training to build invariant representations across multiple treatments at each time step. This results in a breaking of the association between patient history and treatment assignments, allowing for unbiased counterfactual predictions to be made. The model's architecture consists of an encoder-decoder structure:

1. Encoder: Constructs treatment invariant representations using LSTM networks to minimize association biases while maintaining discriminative power for patient outcomes.
2. Decoder: Initialized by the encoder's output, it predicts counterfactual outcomes for sequences of future treatments. This prediction facilitates exploration of treatment timing and selection, effectively assisting clinical decisions.

Experimental Validation

Using a tumor growth model that simulates the effects of chemotherapy and radiotherapy, the superiority of CRN over state-of-the-art methods such as Recurrent Marginal Structural Networks (RMSNs) was demonstrated. The CRN achieved lower normalized root mean squared error (RMSE) in counterfactual predictions under varying confounder biases. Notably, the CRN improved RMSE by over 48.1% compared to its own architecture without adversarial training when confounding effects were severe. Furthermore, CRN achieved higher accuracy in recommending optimal treatment timings.

Implications for Future Developments

The CRN has practical implications in developing clinical decision support systems, particularly in scenarios with complex time-varying treatment sequences. This model's ability to provide confident causal inferences with time-dependent covariates signifies a potential theoretical advancement in causal modeling. It suggests future research directions involving alternative strategies for dealing with time-varying confounders and exploring individualized patient treatment paths in multidimensional settings.

In conclusion, the paper extends the capacities of observational data usage by providing an effective method for longitudinal treatment effect estimation, showing promise in enhancing personalized medical strategies and intervention plans.