Causal Effect Estimation

Causal effect estimation aims to quantify the impact of an intervention on an outcome using observational data, often in the presence of confounding factors. Current research heavily focuses on addressing challenges like hidden confounders and limited data through advanced techniques such as instrumental variable methods, graph neural networks, and doubly robust estimators, often implemented within machine learning frameworks like tree boosting and variational autoencoders. These advancements are crucial for reliable causal inference across diverse fields, improving decision-making in areas ranging from healthcare and economics to social sciences and environmental studies by providing more accurate and robust estimates of treatment effects. The development of privacy-preserving methods further expands the applicability of these techniques to sensitive data.

Papers

November 18, 2024

Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin
Causal Effect Estimation Data Driven Causal Treatment Outcome Prediction Active Causal Counterfactual Sample Effect Estimation

November 14, 2024

MCCE: Missingness-aware Causal Concept Explainer
Jifan Gao, Guanhua Chen
Causal Effect Estimation Unobserved Node Causal Concept

November 11, 2024

Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke
Graph Drawing Causal Inference Causal Relationship Causal Effect Estimation Causal Direction Statistical Relational Parametric Causal

November 2, 2024

Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding
Yufeng Wu, Rohit Bhattacharya
Causal Effect Estimation Graphical Model Intermediate Latent Effect Estimation Network Effect Truncated Contagion Map

October 30, 2024

An Overview of Causal Inference using Kernel Embeddings
Dino Sejdinovic
Causal Inference Causal Relationship Causal Effect Estimation Kernel Hilbert Space Kernel Mean Embeddings

October 28, 2024

Estimating Causal Effects of Text Interventions Leveraging LLMs
Siyi Guo, Myrl G. Marmarelis, Fred Morstatter, Kristina Lerman
Large Language Model Text Modality Causal Effect Causal Effect Estimation Causal Direction Text Block

October 13, 2024

DAG-aware Transformer for Causal Effect Estimation
Manqing Liu, David R. Bellamy, Andrew L. Beam
Causal Inference Causal Structure Causal Effect Estimation Directed Acyclic Graph Cause Effect Structure Aware Transformer

October 7, 2024

Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?
Sherly Alfonso-Sánchez, Kristina P. Sendova, Cristián Bravo
Causal Effect Treatment Effect Causal Effect Estimation Propensity Score Value at Risk

September 13, 2024

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
Xiaojing Du, Feiyu Yang, Wentao Gao, Xiongren Chen
Graph Neural Network Causal Inference Network Programming Causal Effect Estimation Instrumental Variable Latent Confounding

September 11, 2024

Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Chris Holmes
Differential Privacy Privacy Preserving Causal Effect Estimation Information Gain Information Theoretic Privacy

August 26, 2024

Estimating Causal Effects from Learned Causal Networks
Anna Raichev, Alexander Ihler, Jin Tian, Rina Dechter
Causal Model Causal Effect Estimation Probabilistic Graphical Model Causal Direction Causal Query

August 13, 2024

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
Yang Xie, Ziqi Xu, Debo Cheng, Jiuyong Li, Lin Liu, Yinghao Zhang, Zaiwen Feng
Variational Autoencoders Causal Effect Estimation Latent Confounders Variational AutoEncoder Treatment Variable

August 10, 2024

Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Xiangwei Chen, Zexu Sun, Fei Wu, Kun Zhang
Causal Inference Causal Effect Estimation Conditional Average Treatment Effect Instrumental Variable Influential Data

July 15, 2024

Spectral Representation for Causal Estimation with Hidden Confounders
Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai
Causal Effect Estimation Saddle Point Instrumental Variable Text Based Confounders Spectral Representation

July 9, 2024

End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison
Causal Effect Causal Effect Estimation Unstructured Text Data

June 27, 2024

Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Zeqin Yang, Weilin Chen, Ruichu Cai, Yuguang Yan, Zhifeng Hao, Zhipeng Yu, Zhichao Zou, Zhen Peng, Jiecheng Guo
Long Term Causal Effect Estimation Unobserved Confounders Generalization Error Bound Heterogeneous Effect Dose Response

June 25, 2024

Compositional Models for Estimating Causal Effects
Purva Pruthi, David Jensen
Causal Inference Causal Effect Causal Effect Estimation Compositional Model Statistical Relational

June 20, 2024

Causal Inference with Latent Variables: Recent Advances and Future Prospectives
Yaochen Zhu, Yinhan He, Jing Ma, Mengxuan Hu, Sheng Li, Jundong Li
Causal Inference Causal Discovery Latent Variable Causal Effect Recent Advance Causal Effect Estimation

June 9, 2024

Structured Learning of Compositional Sequential Interventions
Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva
Causal Effect Estimation Causal Representation Behavior Prediction Learning Structure Sequential Treatment

June 6, 2024

Causal Estimation of Memorisation Profiles
Pietro Lesci, Clara Meister, Thomas Hofmann, Andreas Vlachos, Tiago Pimentel
Language Model Learning Rate Causal Effect Estimation Memorisation Profile