Parametric Causal

Parametric causal inference focuses on developing methods to estimate causal effects from observational data, often represented using directed acyclic graphs (DAGs). Current research emphasizes efficient algorithms for causal discovery and inference, particularly in high-dimensional or relational settings, employing techniques like lifted inference and kernel methods to handle non-parametric models and complex relationships. These advancements are improving the accuracy and scalability of causal analysis across diverse fields, enabling more robust causal estimations in areas such as personalized systems and social sciences. The development of flexible, semi-parametric models, such as causal-graphical normalizing flows, is also a key focus, allowing for more realistic representations of complex causal systems.

Papers

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

August 20, 2024

Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
Causal Discovery Causal Model Directed Acyclic Graph Acyclic Graph Parametric Causal

March 15, 2024

Lifted Causal Inference in Relational Domains
Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke
Causal Inference Causal Bayesian Network Lifted Inference Relational Domain Parametric Causal

February 7, 2024

Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
Causal Representation Learning General Text Causal Representation Underlying Causal Multiple Distribution Parametric Causal

January 12, 2024

Deep Learning With DAGs
Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou
Deep Learning Causal Inference Causal Effect Causal Relationship Directed Acyclic Graph Parametric Causal Graphical Normalizing Flow

November 21, 2023

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Jikai Jin, Vasilis Syrgkanis
Causal Graph Structural Causal Model Causal Representation Learning Novel Identifiability Test General Environment Parametric Causal

June 1, 2023

Causal Estimation of User Learning in Personalized Systems
Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie
Long Term Causal Effect Causal Effect Estimation Parametric Causal Personalized System Learning Effect

October 24, 2022

Temporally Disentangled Representation Learning
Weiran Yao, Guangyi Chen, Kun Zhang
Latent Causal Parametric Causal

February 7, 2022

Causal Inference Using Tractable Circuits
Adnan Darwiche
Causal Inference Circuit Complexity Tractable Probabilistic Inference Parametric Causal

January 6, 2022

A note on efficient minimum cost adjustment sets in causal graphical models
Ezequiel Smucler, Andrea Rotnitzky
Causal Graph Short Note Parametric Causal