New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences with discovered measures as a treatment or outcome. Our framewo…
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48 results for “text-based causal inference”
Paper tackles treatment leakage in text-based causal inference, proposing methods to mitigate bias.
problem Treatment leakage in text-as-confounder applications introduces bias in causal estimates.
method Formal definitions, four text distillation methods (passage removal, classification, salient feature removal, nullspace projection).
result Moderate distillation optimally balances bias reduction against confounder retention.
C-Learner improves stability of plug-in estimators for causal inference.
problem Limited overlap between treatment and control groups leads to unstable estimates.
method Constrained learning framework that achieves stability and asymptotic properties.
result Constrained learning produces stable estimates with desirable asymptotic properties.
RecoBERT uses a language model to recommend items from catalogs.
problem Harnessing language models for text-based item recommendations.
method RecoBERT is a BERT-based approach that learns specialized language models for item recommendations without requiring labeled data.
result RecoBERT outperforms other techniques in inferring item similarities from textual catalogs.
Optimal text-based indices track VIX and inflation.
problem Maximizing contemporaneous relation or predictive performance with target variables.
method Optimizing text-based indices focusing on VIX and inflation expectations.
result Superior performance compared to existing indices.
Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.Kcs.CL
Study shows text-based news veracity models don't generalize across U.S. and U.K.
problem Generalizability of text-based news veracity detection models across countries.
method Testing news veracity models on U.S. and U.K. news data.
result Text-based classifiers perform poorly when trained on one country's news data and tested on another.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
Probabilistic models can handle causal inference without special tools.
problem Confusion over necessary tools for causal inference.
method Demonstrated through concrete examples that causal questions can be answered using standard probabilistic models.
result Causal questions can be addressed using standard probabilistic modelling and inference.
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success. Recent attempts at solving text-based games with deep reinforcement learning have focused on the latter, i.e., learning to act optimally when valid actions are known in advance. In thi…
The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three addit…
Deep learning aids causal inference in complex settings.
problem Estimating heterogeneous treatment effects in non-linear, time-varying, and encoded confounders.
method Intuitive introduction to deep learning and causal inference, focusing on observational data.
result Maximizes accessibility to causal inference through deep learning.
Book introduces ML and AI for causal inference.
problem Uncertainty in causal relationships.
method Structural equation models, DAGs, SCMs, and Double/Debiased Machine Learning.
result Improved inference in causal models using predictive tools.
Private Causal Inferencestat.ML
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…
Two environments are enough to infer causal graphs and counterfactuals.
problem Inferring causal relations from multiple environments, especially for nonlinear mechanisms.
method Using structural causal models and the invariance principle, the study shows that only two auxiliary environments are sufficient for causal graph inference and counterfactual inference.
result Two auxiliary environments are sufficient for identifying causal graphs and counterfactuals.
ABCI infers causal models and queries simultaneously using Bayesian active learning.
problem Inference of causal models and effects in a two-stage process is inefficient and unnatural.
method Active Bayesian Causal Inference (ABCI) using Gaussian processes for sequentially designing experiments.
result ABCI is more data-efficient and accurate in learning causal queries from fewer samples.
Variational Causal Networks approximate Bayesian inference over causal structures.
problem Quantifying uncertainty in causal structure inference from finite data.
method Parametric variational family over DAGs, using Evidence Lower Bound (ELBO) for tractable learning.
result Approximation of the true posterior over DAGs is demonstrated to be good.
TBIP uses texts to quantify lawmakers' political positions.
problem Quantifying lawmakers' political positions from speeches, tweets, etc.
method Unsupervised probabilistic topic model analyzing texts.
result TBIP separates lawmakers by party and infers ideal points close to vote-based.
DECI combines causal discovery and inference in a single model for diverse data types.
problem Combining causal discovery and inference methods for diverse data types.
method Develops a single flow-based non-linear additive noise model (DECI) for causal discovery and inference.
result DECI can recover ground truth causal graphs and perform (C)ATE estimation.
BBCI uses meta prediction to estimate causal effects from datasets.
problem Estimating causal effects from observed data.
method Meta prediction to learn causal effect estimation.
result BBCI accurately estimates ATEs and CATEs across various causal inference problems.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
Causal inference is similar to prediction with treatment bias.
problem Generalizing from labeled to unlabeled data with treatment effects.
method Reframing causal inference as a prediction problem with explicit assumptions.
result Causal assumptions are not uniquely strong but more explicit.
Deep learning method infers causal interactions from data.
problem Causal inference from observational data.
method Transform input vectors to NEPDFs, train CNN on NEPDFs.
result Improves upon prior methods for causal inference.
Blog post comparing neural network methods for causal inference.
problem Estimating heterogeneous treatment effects in causal inference.
method Developed and compared a fully connected neural network implementation of Bayesian Causal Forest.
result Improvements in performance in simulation settings.
RealCause provides a realistic benchmark for causal inference.
problem Lack of a reliable benchmark for comparing causal effect estimators.
method Flexible generative models to create a benchmark that is both ground-truth and realistic.
result Evaluation of over 1500 causal estimators provides evidence for choosing hyperparameters using predictive metrics.
Paper constructs unfaithful probability distributions in binary causal graphs.
problem Unfaithful probability distributions in binary causal graphs.
method Constructs unfaithful probability distributions in binary causal graphs.
result Examples of unfaithful probability distributions in binary causal graphs.
Bayesian causal inference method improves accuracy over traditional approaches.
problem Bayesian marginalisation over causal models is computationally infeasible.
method Decomposes structure marginalisation into causal orders and DAGs, using Gaussian processes for mechanisms and ARCO for orders.
result Method outperforms state-of-the-art in structure learning and inference.
ParKCa combines multiple causal inference methods to infer new causes from known and unknown factors.
problem Causal inference from observational data when randomized experiments are not feasible.
method ParKCa uses a stacking approach to combine results from multiple causal inference methods.
result ParKCa infers more causes than existing methods in real-world and simulated datasets.
New method infers causal effects without knowing control variables.
problem Inference errors when control variables are unknown.
method Proposes a method for inferring causal effects when control variables are unknown.
result Proves method yields asymptotically valid confidence intervals for average causal effects.
CInA method uses attention to improve causal inference.
problem Challenges in causal inference, especially in complex tasks.
method CInA method utilizes self-supervised causal learning with multiple unlabeled datasets and transformer-type architecture.
result CInA effectively generalizes to out-of-distribution datasets and various real-world datasets.
This survey explores causal inference in banking, finance, and insurance.
problem Explaining decisions in banking, finance, and insurance using causal inference.
method Categorizes 37 papers on causal inference applications in banking, finance, and insurance.
result Causal inference is still in its infancy in banking and insurance sectors.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Gamescs.LG
Improved RL for TBGs by pruning irrelevant tokens and bootstrapping.
problem RL methods fail to generalize in TBGs with small data.
method CREST for irrelevant token removal, bootstrapped Q-learning.
result Improved generalization in unseen TextWorld games.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
problem Stability of causal direction inference results from observational data.
method Parallel ensemble frameworks to map and improve inference accuracy.
result Significant improvement in accuracy of causal direction inference.
Transformer model handles causal inference with DAG integration.
problem Complex causal structures and adaptability across various scenarios.
method Integrates DAGs into transformer's attention mechanism.
result Surpasses existing methods in estimating causal effects.
Novel framework combines tree-based discretization and ILP matching for causal inference.
problem Challenges in identifying causal relationships from observational data.
method Combines tree-based discretization and ILP matching for causal inference.
result Yields computational efficiency and less biased ATT estimates.
Interpretable model for Granger causality using neural networks.
problem Inferring Granger causality in complex dynamical systems.
method Extension of self-explaining neural networks for multivariate Granger causality.
result Framework performs on par with baseline methods and better at inferring interaction signs.
Graph neural networks help infer causal effects from partially observable data.
problem Inferring causal effects from partially observable data.
method Theoretical analysis of GNN and SCM connections.
result Established a new model class for GNN-based causal inference.
CausalML simplifies causal inference methods in Python.
problem Combining causal inference and machine learning.
method Collection of causal inference methods in Python.
result Makes causal inference methods accessible in Python.
Amortized Causal Discovery learns to infer causal graphs from time-series data, improving performance.
problem Inference of causal graphs from time-series data is inefficient due to fitting new models for each sample.
method Proposes Amortized Causal Discovery, a variational model that leverages shared dynamics across samples with different causal graphs.
result Significant improvements in causal discovery performance demonstrated experimentally.
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
problem Challenges in principled Bayesian inference for causal effects.
method Generalized Bayesian framework that places priors directly on causal estimands and updates using identification-driven loss functions.
result Yields generalized posteriors for causal effects with uncertainty quantification.
Geometric Causal Modelsstat.ML
Develops geometric causal models for causal inference from dependent data.
problem Causal inference from structured, dependent data (e.g., spatial, network, molecular).
method Geometric causal models (GCMs) exploiting symmetries of data generating process, combining group theory, ergodic theory, and Bayesian inference.
result Establishes identification and estimation of causal effects from dependent data.
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose …
Unified framework for causal inference with reliable uncertainty quantification.
problem Causal inference under unobserved confounding with unreliable uncertainty quantification.
method Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning.
result Strong predictive performance and informative uncertainty quantification.
Proposes a method to infer causal effects from incomplete data using latent confounders.
problem Missing data complicates causal inference, especially for non-linear models.
method Uses variational autoencoders to learn latent confounders and incorporate missing values.
result Demonstrates effectiveness of the method, especially for non-linear models.
Paper introduces active and passive causal inference techniques.
problem Causal inference in machine learning.
method Categorizes causal inference techniques into active and passive approaches.
result Describes and discusses various causal inference methods.
BaCaDI discovers causal structures from unknown interventions.
problem Inferring causal structures from unknown interventions with limited data.
method Bayesian framework with gradient-based variational inference.
result BaCaDI outperforms related methods in identifying causal structures and intervention targets.
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
New method infers causal relationships from nonstationary time series data.
problem Challenges in inferring causal relationships from nonstationary time series data.
method Proposes a new class of restricted SCM with time-varying filters and stationary noise, leveraging asymmetry from nonstationarity.
result Demonstrates effectiveness of the proposed methodology on various synthetic and real datasets.