4 papers · 1 filter
Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method
Kairong Han, Weidong Huang, Taiyang Zhou +2
In the online ride-hailing pricing context, companies often conduct randomized controlled trials (RCTs) and utilize uplift models to assess the effect of discounts on customer orde…
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
Ruichu Cai, Junjie Wan, Weilin Chen +4
Estimating long-term causal effects by combining long-term observational and short-term experimental data is a crucial but challenging problem in many real-world scenarios. In exis…
Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
Jiahong Li, Zeqin Yang, Jixing Xu +4
Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast…
DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with Limited Data
Jiaqi Yang, Enming Liang, Zicheng Su +5
Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize (PO) framework by training predictive models directly on decision loss (DL), enhancing de…