Exponential Family

Latest papers

13 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Exponential Family Synthetic Controls
Sep 21, 2026Hector Rodriguez-Deniz, David M. BleiCausal Effect Estimation Exponential Family
Data Science Institute Columbia University New York, NY 10027, USA · Department of Computer Science and Department of Statistics Columbia University New York, NY 10027, USA
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sep 16, 2026Sambit Mishra, Urbashi MitraStructural Causal Models Causal Discovery
Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles, CA 90089, USA
Generalised Score Matching on Convex Domains
Sep 10, 2026Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar SeelamantulaScore Matching Parameter Estimation
Department of Electrical Engineering Indian Institute of Science, Bengaluru 560012
Tensor network representations of discrete maximum entropy distributions via mean polytopes
Sep 7, 2026Alex Goessmann, Martin EigelTensor Networks Exponential Family
Weierstrass Institute of Applied Analysis and Stochastics Anton-Wilhelm-Amo-Straße 39 Berlin, 10117, Germany
Proportional Analogies on Probability Distributions via Bayesian Updating
Aug 12, 2026Pierre-Alexandre MurenaAnalogical Reasoning Exponential Family
Hamburg University of Technology (TUHH) Research Group on Human-Centric Machine Learning
A Theoretical Interpretation of In-Context Learning via Probabilistic Modeling
Jun 27, 2026Zhenyu Liu, Huaze Tang, Shao-Lun HuangIn-Context Learning Exponential Family
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Interpreting FCDNNs via RG on Exponential Family
May 29, 2026Fuzhou Gong, Zigeng XiaNeural Network Interpretability Neural Network Training Dynamics
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. · School of Mathematics, Northwest University, Xi’an 710127, China.
Finite Sample Bounds for Learning with Score Matching
May 13, 2026Devin Smedira, Abhijith Jayakumar, Sidhant Misra +2Score Matching Sample Complexity
Operations Research Center, Massachusetts Institute of Technology · Theoretical division, Los Alamos National Lab
Exponential families from a single KL identity
Apr 30, 2026Marc DymetmanInformation Geometry Exponential Family
Scientific Consultant
Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models
Mar 21, 2026Anish LakkapragadaExponential Family Classification
Yale University, New Haven, CT 06511
Exponential-Family Membership Inference: From LiRA and RMIA to BaVarIA
Mar 12, 2026Rickard BrännvallExponential Family Membership Inference Attacks
RISE Research Institutes of Sweden
Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
Mar 2, 2026Amir Asiaee, Samhita PalExponential Family Differential Privacy
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
It's all In the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms
Jan 29, 2026Keegan Kang, Kerong Wang, Ding Zhang +3Parameter Estimation Exponential Family
Department of Mathematics and Statistics, Bucknell University, USA · School of Data Science, University of Virginia, USA · Department of CSE, IIT Hyderabad, India +2

Sep 21, 2026J/K move · Enter open · S save

Exponential Family Synthetic Controls

Hector Rodriguez-Deniz, David M. Blei

Data Science Institute Columbia University New York, NY 10027, USA · Department of Computer Science and Department of Statistics Columbia University New York, NY 10027, USA

Causal Effect Estimation Exponential Family

Open paper AskPDF arXiv

We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the panel corresponds to a dataset drawn from an exponential family whose natural parameters factorize probabilistically across units and times. We estimate the latent factors using black-box variational inference. This replaces the usual weighted-average view of synthetic controls with a flexible probabilistic model that operates on full distributions. We propose causal estimands based on divergences between pre- and post-intervention distributions induced by the posterior of the natural parameters, together with distributional placebo tests to support causal inference and assess the significance of the estimated effects. We validate the proposed framework on synthetic and real data. Across a variety of exponential-family distributions, EFSC accurately recovers causal effects induced by exponential tilts, together with the corresponding divergences between treated and counterfactual distributions. The framework also captures effects induced by structural perturbations of the latent factors and by heavy-tailed noise contamination. Finally, we apply EFSC to study the expansion of Medicaid under the Affordable Care Act (ACA) and its impact on the distribution of health insurance coverage across U.S. states. Code is available at https://github.com/blei-lab/efsc.