Counterfactual Inference
58 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Most implemented papers
Deep Kalman Filters
Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.
Deep Structural Causal Models for Tractable Counterfactual Inference
biomedia-mira/deepscm
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NeurIPS 2020
We formulate a general framework for building structural causal models (SCMs) with deep learning components.
SurvCaus : Representation Balancing for Survival Causal Inference
abraich/pycaus
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Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.
Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
patrickrchao/diffusionbasedcausalmodels
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We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.
Learning Representations for Counterfactual Inference
sschrod/adbcr
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Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.
RNN-based counterfactual prediction, with an application to homestead policy and public schooling
This paper proposes a method for estimating the effect of a policy intervention on an outcome over time.
Counterfactual Mean Embeddings
In this work, we propose to model counterfactual distributions using a novel Hilbert space representation called counterfactual mean embedding (CME).
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.
Counterfactual Inference for Consumer Choice Across Many Product Categories
One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data.
Counterfactual diagnosis
We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.
