🤖 AI Summary
This paper addresses the challenge of jointly estimating multiple causal effects—such as multidimensional price response curves—from observational data alone, under incentive strategy optimization, to enhance modeling robustness and decision effectiveness in distributionally anomalous settings.
Method: We propose the first deep monotonic neural network framework capable of simultaneously modeling multiple causal effects, integrating monotonicity constraints, multi-task learning, and counterfactual estimation. We further introduce three empirically verifiable prior conditions that theoretically characterize the applicability boundary of observational data for causal evaluation.
Contribution/Results: Evaluated via offline benchmarking and online A/B testing, our model achieves significant improvements in prediction accuracy and strategy ROI. The results empirically validate both the feasibility and necessity of joint multi-causal-effect modeling without randomized controlled trials (RCTs), advancing causal inference for real-world incentive design.
📝 Abstract
When we plan to use money as an incentive to change the behavior of a person (such as making riders to deliver more orders or making consumers to buy more items), the common approach of this problem is to adopt a two-stage framework in order to maximize ROI under cost constraints. In the first stage, the individual price response curve is obtained. In the second stage, business goals and resource constraints are formally expressed and modeled as an optimization problem. The first stage is very critical. It can answer a very important question. This question is how much incremental results can incentives bring, which is the basis of the second stage. Usually, the causal modeling is used to obtain the curve. In the case of only observational data, causal modeling and evaluation are very challenging. In some business scenarios, multiple causal effects need to be obtained at the same time. This paper proposes a new observational data modeling and evaluation framework, which can simultaneously model multiple causal effects and greatly improve the modeling accuracy under some abnormal distributions. In the absence of RCT data, evaluation seems impossible. This paper summarizes three priors to illustrate the necessity and feasibility of qualitative evaluation of cognitive testing. At the same time, this paper innovatively proposes the conditions under which observational data can be considered as an evaluation dataset. Our approach is very groundbreaking. It is the first to propose a modeling framework that simultaneously obtains multiple causal effects. The offline analysis and online experimental results show the effectiveness of the results and significantly improve the effectiveness of the allocation strategies generated in real world marketing activities.