The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
Who should we prioritize for intervention when we cannot estimate intervention effects? In many applied domains (e.g., advertising, customer retention, and behavioral nudging) prioritization is guided by predictive models that estimate outcome probabilities rather than causal effects. This paper investigates when these predictions (scores) can effectively rank individuals by their intervention effects, particularly when direct effect estimation is infeasible or unreliable. We propose a conceptual framework based on amenability: an individual's latent proclivity to be influenced by an intervention. We then formalize conditions under which predictive scores serve as effective proxies for amenability. These conditions justify using non-causal scores for intervention prioritization, even when the scores do not directly estimate effects. We further show that, under plausible assumptions, predictive models can outperform causal effect estimators in ranking individuals by intervention effects. Empirical evidence from an advertising context supports our theoretical findings, demonstrating that predictive modeling can offer a more robust approach to targeting than effect estimation. Our framework suggests a shift in focus, from estimating effects to inferring who is amenable, as a practical and theoretically grounded strategy for prioritizing interventions in resource-constrained environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
Who should we prioritize for intervention when we cannot estimate intervention effects? In many applied domains -- such as advertising, customer retention, and behavioral nudging -- prioritization is guided by predictive…
Causal InferenceDecision MakingLearning-To-RankLearning Directed Acyclic Graphs from Partial Orderings
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without addit…
Conditional independences and causal relations implied by sets of equations
Real-world complex systems are often modelled by sets of equations with endogenous and exogenous variables. What can we say about the causal and probabilistic aspects of variables that appear in these equations without e…
Causal DiscoveryA direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating pr…
Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
Ordering-based approaches to causal discovery identify topological orders of causal graphs, providing scalable alternatives to combinatorial search methods. Under the Additive Noise Model (ANM) assumption, recent causal …
Prompt Engineering