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Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

9 July 2017

Papers citing "Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition"

50 / 113 papers shown

Title
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects Zhongyuan Liang L. Laan Ahmed Alaa 14 0 0 16 Jun 2025
Honesty in Causal Forests: When It Helps and When It Hurts Yanfang Hou Carlos Fernández-Loría 9 0 0 16 Jun 2025
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning Vahid Balazadeh Hamidreza Kamkari Valentin Thomas Benson Li Junwei Ma Jesse C. Cresswell Rahul G. Krishnan CML 15 0 0 09 Jun 2025
ReLU integral probability metric and its applications Yuha Park Kunwoong Kim Insung Kong Yongdai Kim 93 0 0 26 Apr 2025
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series Gideon Stein M. Shadaydeh Jan Blunk Niklas Penzel Joachim Denzler AI4TS 78 1 0 21 Mar 2025
KANITE: Kolmogorov-Arnold Networks for ITE estimation Eshan Mehendale Abhinav Thorat Ravi Kolla N. Pedanekar CML 116 1 0 18 Mar 2025
CausalMan: A physics-based simulator for large-scale causality Nicholas Tagliapietra J. Luettin Lavdim Halilaj Moritz Willig Tim Pychynski Kristian Kersting CML 107 0 0 18 Feb 2025
Orthogonal Representation Learning for Estimating Causal Quantities Valentyn Melnychuk Dennis Frauen Jonas Schweisthal Stefan Feuerriegel CML OOD BDL 136 2 0 06 Feb 2025
Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates Baozhen Wang Xingye Qiao 201 2 0 28 Jan 2025
Oblique Bayesian additive regression trees Paul-Hieu V. Nguyen Ryan Yee Sameer K. Deshpande 57 0 0 13 Nov 2024
Very fast Bayesian Additive Regression Trees on GPU Giacomo Petrillo 71 0 0 30 Oct 2024
DAG-aware Transformer for Causal Effect Estimation Manqing Liu David R. Bellamy Andrew L. Beam CML 53 5 0 13 Oct 2024
Towards Representation Learning for Weighting Problems in Design-Based Causal Inference Oscar Clivio Avi Feller Chris Holmes CML OOD 72 3 0 24 Sep 2024
Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression Anpeng Wu Kun Kuang Ruoxuan Xiong Xiangwei Chen Zexu Sun Fei Wu Kun Zhang CML 74 0 0 10 Aug 2024
Distilling interpretable causal trees from causal forests Patrick Rehill CML 66 0 0 02 Aug 2024
Robust CATE Estimation Using Novel Ensemble Methods Oshri Machluf Tzviel Frostig Gal Shoham T. Milo Elad Berkman Raviv Pryluk CML 81 0 0 04 Jul 2024
Improve ROI with Causal Learning and Conformal Prediction Meng Ai Zhuo Chen Jibin Wang Jing Shang Tao Tao Zhen Li 90 1 0 01 Jul 2024
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark Fredrik D. Johansson CML 62 0 0 25 May 2024
Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck Hao Yang Zexu Sun Hongteng Xu Xu Chen 105 3 0 24 May 2024
Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis Daniel Csillag C. Struchiner G. Goedert OOD CML 74 2 0 15 May 2024
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules Michael Lingzhi Li Kosuke Imai CML 89 0 0 25 Apr 2024
Unveiling the Potential of Robustness in Evaluating Causal Inference Models Yiyan Huang Cheuk Hang Leung Siyi Wang Yijun Li Qi Wu OOD CML 76 0 0 28 Feb 2024
Federated Learning for Estimating Heterogeneous Treatment Effects Disha Makhija Joydeep Ghosh Yejin Kim CML FedML 94 2 0 27 Feb 2024
The CATT SATT on the MATT: semiparametric inference for sample treatment effects on the treated Andrew Yiu CML 142 0 0 08 Feb 2024
Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects Jef Jonkers Jarne Verhaeghe Glenn Van Wallendael Luc Duchateau Sofie Van Hoecke 456 2 0 07 Feb 2024
The Essential Role of Causality in Foundation World Models for Embodied AI Tarun Gupta Wenbo Gong Chao Ma Nick Pawlowski Agrin Hilmkil ... Jianfeng Gao Stefan Bauer Danica Kragic Bernhard Schölkopf Cheng Zhang 79 17 0 06 Feb 2024
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding Xinyuan Chen Liangyuan Hu Fan Li CML 93 1 0 04 Feb 2024
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation Lucile Ter-Minassian Liran Szlak Ehud Karavani Chris Holmes Y. Shimoni 75 0 0 31 Jan 2024
Proximal Causal Inference With Text Data Jacob M. Chen Rohit Bhattacharya Katherine A. Keith 68 2 0 12 Jan 2024
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees Weijia Zhang Chun Kai Ling Xuanhui Zhang CML OOD 72 10 0 24 Dec 2023
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model Shunsuke Horii Yoichi Chikahara 58 4 0 16 Dec 2023
SpaCE: The Spatial Confounding Environment Mauricio Tec A. Trisovic Michelle Audirac Sophie Woodward Jie Kate Hu N. Khoshnevis Francesca Dominici CML 86 3 0 01 Dec 2023
Adversarial Distribution Balancing for Counterfactual Reasoning Stefan Schrod Fabian H. Sinz Michael Altenbuchinger OOD CML 88 1 0 28 Nov 2023
Causal Q-Aggregation for CATE Model Selection Hui Lan Vasilis Syrgkanis CML 94 4 0 25 Oct 2023
Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability Patrick Rehill Nicholas Biddle CML ELM 74 4 0 20 Oct 2023
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data Mouad El Bouchattaoui Myriam Tami Benoit Lepetit P. Cournède CML OOD 212 2 0 16 Oct 2023
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning Michael Lingzhi Li Kosuke Imai CML 54 1 0 12 Oct 2023
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects Ahmed Alaa Zaid Ahmad Mark van der Laan CML 197 16 0 28 Aug 2023
RCT Rejection Sampling for Causal Estimation Evaluation Katherine A. Keith Sergey Feldman David Jurgens Jonathan Bragg Rohit Bhattacharya CML 77 7 0 27 Jul 2023
Semiparametric posterior corrections Andrew Yiu Edwin Fong Chris Holmes Judith Rousseau 218 4 0 09 Jun 2023
On Mixing Rates for Bayesian CART Jungeum Kim Veronika Rockova 120 7 0 31 May 2023
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation V. Chauhan Jiandong Zhou Ghadeer O. Ghosheh Soheila Molaei David Clifton 79 11 0 25 May 2023
Covariate balancing using the integral probability metric for causal inference Insung Kong Yuha Park Joonhyuk Jung Kwonsang Lee Yongdai Kim 99 8 0 23 May 2023
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 69 26 0 06 Feb 2023
How to select predictive models for causal inference? M. Doutreligne Gaël Varoquaux ELM CML 70 2 0 01 Feb 2023
Estimating Causal Effects using a Multi-task Deep Ensemble Ziyang Jiang Zhuoran Hou Yi-Ling Liu Yiman Ren Keyu Li David Carlson CML 76 6 0 26 Jan 2023
Meta-analysis of individualized treatment rules via sign-coherency Jay Jojo Cheng J. Huling Guanhua Chen 94 0 0 28 Nov 2022
Propensity score models are better when post-calibrated R. Gutman Ehud Karavani Y. Shimoni 90 4 0 02 Nov 2022
A Mixing Time Lower Bound for a Simplified Version of BART Omer Ronen Theo Saarinen Yan Shuo Tan James Duncan Bin Yu 51 10 0 17 Oct 2022
A Survey of Deep Causal Models and Their Industrial Applications Zongyu Li Xiaoning Guo Siwei Qiang CML AI4CE 76 8 0 19 Sep 2022