37 papers with code • 0 benchmarks • 2 datasets
These leaderboards are used to track progress in Counterfactual Inference
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
We formulate a general framework for building structural causal models (SCMs) with deep learning components.
SurvCaus : Representation Balancing for Survival Causal Inference
Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years.
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.
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.
We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
In contrast, structural causal models support counterfactual inference, but do not identify the mechanisms.
Counterfactual VQA: A Cause-Effect Look at Language Bias
VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.
Learning Decomposed Representation for Counterfactual Inference
The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing.