Evaluating Predictive Performance of Decision Policies Under Confounding

Core Concepts

The core message of this article is to develop a framework for comparing the predictive performance of decision policies under confounding, where the status quo policy depends on unobserved factors. The authors propose a novel partial identification technique that yields informative bounds on predictive performance differences by isolating comparison-relevant uncertainty.

Abstract

The article addresses the challenge of comparing the predictive performance of a proposed decision policy against an existing status quo policy, when the status quo policy depends on unobserved confounding factors.
Key highlights:

Existing off-policy evaluation frameworks do not support comparison of predictive performance metrics, as they typically target the expected potential outcome rather than fine-grained predictive performance measures.
The authors formulate the problem of comparative predictive performance evaluations for decision making policies under uncertainty.
They propose a novel partial identification technique that yields informative bounds on predictive performance differences by isolating comparison-relevant uncertainty.
The technique is interoperable with a range of modern identification approaches from causal inference and off-policy evaluation literature, such as instrumental variable, marginal sensitivity model, and proximal variable.
The authors develop flexible methods for finite sample estimation of regret bounds under no parametric assumptions on the confounded status quo policy.
They validate the framework theoretically and via synthetic data experiments, and demonstrate its application in a real-world healthcare enrollment policy evaluation.

Stats

"Predictive models are often introduced under the rationale that they improve performance over an existing decision-making policy (Grove et al., 2000)."
"Given the high-stakes nature of these domains, regulatory frameworks have called for organizations to provide explicit comparisons of predictive models against the status quo they are intended to replace (Wyden; Johnson & Zhang, 2022)."
"Existing off-policy evaluation frameworks do not support comparison of predictive performance metrics in the settings outlined above."

Quotes

"Predictive models are often introduced under the rationale that they improve performance over an existing decision-making policy (Grove et al., 2000)."
"Given the high-stakes nature of these domains, regulatory frameworks have called for organizations to provide explicit comparisons of predictive models against the status quo they are intended to replace (Wyden; Johnson & Zhang, 2022)."

Key Insights Distilled From

Predictive Performance Comparison of Decision Policies Under Confounding

by Luke Guerdan... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00848.pdf

Deeper Inquiries

How can this framework be extended to settings with multiple confounding factors or complex causal structures?

In settings with multiple confounding factors or complex causal structures, the framework can be extended by incorporating more sophisticated identification strategies. One approach is to use instrumental variables (IV) or structural equation modeling to handle multiple confounders. By including additional variables that act as instruments for the confounders, researchers can disentangle the effects of different factors on the outcome of interest. Additionally, techniques like propensity score matching or inverse probability weighting can be used to address complex causal structures by balancing the distribution of confounders across different treatment groups. These methods help ensure that the comparison of decision policies is not biased by the presence of multiple confounding factors or intricate causal relationships.

What are the potential limitations or drawbacks of relying on causal assumptions to bound policy comparisons?

While causal assumptions are essential for bounding policy comparisons, they come with certain limitations and drawbacks. One limitation is the potential for misspecification of the causal model, leading to biased estimates of policy performance. If the underlying assumptions do not hold in the real-world data, the results of the policy comparison may be misleading. Additionally, causal assumptions can be restrictive and may not capture the full complexity of the decision-making process. This can limit the generalizability of the findings to different contexts or populations. Moreover, causal assumptions often require strong assumptions about the data-generating process, which may not always be realistic or verifiable in practice. Finally, relying solely on causal assumptions may overlook other sources of uncertainty or bias in the policy comparison, leading to incomplete or inaccurate results.

How might this framework be adapted to support comparisons of decision policies in dynamic or sequential settings?

To adapt this framework for comparisons of decision policies in dynamic or sequential settings, researchers can incorporate techniques from reinforcement learning and sequential decision-making. One approach is to use dynamic treatment regimes or adaptive interventions, where decisions are made sequentially based on the evolving state of the system. By modeling the sequential nature of decision-making, researchers can evaluate the performance of policies over time and assess their impact on outcomes. Additionally, techniques like contextual bandits or multi-armed bandits can be used to optimize decision policies in real-time based on feedback from previous decisions. By integrating these dynamic and sequential elements into the framework, researchers can support more nuanced and adaptive comparisons of decision policies in complex and evolving environments.