Causal Inference
425 papers with code • 3 benchmarks • 8 datasets
Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.
( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )
Libraries
Use these libraries to find Causal Inference models and implementations
IHDP
NTPairs
ICLR Database
Latest papers with no code
Relationship Discovery for Drug Recommendation
Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs.
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available.
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions.
Evaluating Interventional Reasoning Capabilities of Large Language Models
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Social science research often hinges on the relationship between categorical variables and outcomes.
Context-dependent Causality (the Non-Nonotonic Case)
We develop a novel identification strategy as well as a new estimator for context-dependent causal inference in non-parametric triangular models with non-separable disturbances.
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
Propensity Score Alignment of Unpaired Multimodal Data
Multimodal representation learning techniques typically rely on paired samples to learn common representations, but paired samples are challenging to collect in fields such as biology where measurement devices often destroy the samples.
Predictive Performance Comparison of Decision Policies Under Confounding
However, it is challenging to compare predictive performance against an existing decision-making policy that is generally under-specified and dependent on unobservable factors.
C-XGBoost: A tree boosting model for causal effect estimation
Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data.
