Causal Inference
424 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
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
juangamella/causal-chamber-paper
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The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields.
Vision-and-Language Navigation via Causal Learning
In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their performance in unseen environments.
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution.
Causal Inference for Human-Language Model Collaboration
xmubq/dtr-text
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A key challenge in answering this causal inference question is formulating an appropriate causal estimand: the conventional average treatment effect (ATE) estimand is inapplicable to text-based treatments due to their high dimensionality.
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear.
Semi-Supervised Learning for Deep Causal Generative Models
Training causal generative models that address such counterfactual questions, though, currently requires that all relevant variables have been observed and that corresponding labels are available in training data.
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
Then, assuming the nuisance functions are H\"{o}lder smooth, but without assuming knowledge of the true smoothness level or the covariate density, we establish that DCDR estimators with several linear smoothers are semiparametric efficient under minimal conditions and achieve fast convergence rates in the non-$\sqrt{n}$ regime.
Lifted Causal Inference in Relational Domains
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets.
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets.
