Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments
本文提出CIHSI-Net框架，采用BFG-WB方法解决多治疗条件下因果推断中的估计方差问题，同时保持局部邻近结构，降低计算复杂度。
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TruncProof: A Guardrail for LLM-based JSON Generation under Token-Length Constraints
This work addresses the challenge that large language models face in generating syntactically valid structured outputs—such as JSON—under strict token-length constraints, often resulting in either truncated invalid sequences or unbounded generation. To resolve this, the authors propose TruncProof, a novel approach that integrates LL(1) grammar parsing with token-length awareness during decoding. At each generation step, TruncProof dynamically estimates the minimum number of tokens required to complete a syntactically valid JSON object and incorporates this constraint into advanced decoding strategies such as beam search. The method achieves 100% syntactic correctness in generated JSON under stringent length limits while preserving high semantic fidelity, thereby overcoming a key limitation of existing techniques that fail to simultaneously ensure structural validity and controllable output length.
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Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
This work addresses the challenge of off-policy evaluation under right-censored survival outcomes, where existing methods are prone to systematic bias and yield inaccurate policy value estimates. To mitigate this issue, the study introduces inverse probability of censoring weighting (IPCW) into off-policy evaluation for the first time, proposing two novel estimators—IPCW-IPS and IPCW-DR—that are both unbiased and doubly robust, effectively correcting for censoring-induced bias. Furthermore, the proposed framework naturally extends to policy optimization under budget constraints. Experimental results on both synthetic and real-world datasets demonstrate that the method substantially improves the accuracy of policy evaluation and enhances learning performance in censored environments.
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Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
Estimating individual treatment effects and treatment–treatment interactions in multi-treatment settings faces two key challenges: insufficient parameter sharing across correlated treatments and exacerbated selection bias due to redundant latent variable modeling. To address these, we propose a unified framework integrating task embedding and balanced representation learning. A task embedding network enables parameter sharing across treatment modalities, while a nonparametric representation learning network—regularized by a learnable balancing penalty—avoids unnecessary latent variables, jointly mitigating confounding bias and selection bias. Our method synergistically combines deep learning, variational autoencoders, and learnable balancing constraints. In extensive synthetic experiments, it significantly outperforms state-of-the-art baselines. On real-world marketing data, it demonstrates high accuracy in estimating causal effects of multi-treatment combinations and strong practical deployability.
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Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments
本文提出CIHSI-Net框架，采用BFG-WB方法解决多治疗条件下因果推断中的估计方差问题，同时保持局部邻近结构，降低计算复杂度。
0 citationsRead paper
TruncProof: A Guardrail for LLM-based JSON Generation under Token-Length Constraints
This work addresses the challenge that large language models face in generating syntactically valid structured outputs—such as JSON—under strict token-length constraints, often resulting in either truncated invalid sequences or unbounded generation. To resolve this, the authors propose TruncProof, a novel approach that integrates LL(1) grammar parsing with token-length awareness during decoding. At each generation step, TruncProof dynamically estimates the minimum number of tokens required to complete a syntactically valid JSON object and incorporates this constraint into advanced decoding strategies such as beam search. The method achieves 100% syntactic correctness in generated JSON under stringent length limits while preserving high semantic fidelity, thereby overcoming a key limitation of existing techniques that fail to simultaneously ensure structural validity and controllable output length.
0 citationsRead paper
Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
This work addresses the challenge of off-policy evaluation under right-censored survival outcomes, where existing methods are prone to systematic bias and yield inaccurate policy value estimates. To mitigate this issue, the study introduces inverse probability of censoring weighting (IPCW) into off-policy evaluation for the first time, proposing two novel estimators—IPCW-IPS and IPCW-DR—that are both unbiased and doubly robust, effectively correcting for censoring-induced bias. Furthermore, the proposed framework naturally extends to policy optimization under budget constraints. Experimental results on both synthetic and real-world datasets demonstrate that the method substantially improves the accuracy of policy evaluation and enhances learning performance in censored environments.
0 citationsRead paper
Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
Estimating individual treatment effects and treatment–treatment interactions in multi-treatment settings faces two key challenges: insufficient parameter sharing across correlated treatments and exacerbated selection bias due to redundant latent variable modeling. To address these, we propose a unified framework integrating task embedding and balanced representation learning. A task embedding network enables parameter sharing across treatment modalities, while a nonparametric representation learning network—regularized by a learnable balancing penalty—avoids unnecessary latent variables, jointly mitigating confounding bias and selection bias. Our method synergistically combines deep learning, variational autoencoders, and learnable balancing constraints. In extensive synthetic experiments, it significantly outperforms state-of-the-art baselines. On real-world marketing data, it demonstrates high accuracy in estimating causal effects of multi-treatment combinations and strong practical deployability.
0 citationsRead paper