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Found 82 papers, 46 papers with code
Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
Constructing confidence intervals (CIs) for the average treatment effect (ATE) from patient records is crucial to assess the effectiveness and safety of drugs.
Slowing Down Forgetting in Continual Learning
A common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned.
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable.
Causal machine learning for predicting treatment outcomes
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.
DiffPO: A causal diffusion model for learning distributions of potential outcomes
In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes.
Learning Representations of Instruments for Partial Identification of Treatment Effects
Our contributions are three-fold: (1) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space so that we yield valid bounds on the CATE.
Stabilized Neural Prediction of Potential Outcomes in Continuous Time
The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps.
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing
Knowledge tracing (KT) is a popular approach for modeling students' learning progress over time, which can enable more personalized and adaptive learning.
A Fused Large Language Model for Predicting Startup Success
Using 20, 172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power.
Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
We then provide a comprehensive theoretical analysis that characterizes the different learners and that allows us to offer insights into when specific learners are preferable.
Conformal Prediction for Causal Effects of Continuous Treatments
To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data.
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
Specifically, we show that current assumptions from the literature on multiple environments allow us to interpret the environment as an instrumental variable (IV).
G-Transformer for Conditional Average Potential Outcome Estimation over Time
In order to address both limitations, we introduce the G-transformer (GT).
Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
Finally, we discuss the applicability of the model to other chronic and long-lasting diseases.
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Then, we demonstrate our framework using real-world HIV data.
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions
Furthermore, we mathematically analyze the convergence rate of parameters and the convergence rate in value (i. e., the training loss).
Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing
However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media.
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
This enables practitioners to examine the sensitivity of their machine learning models to unobserved confounding in fairness-critical applications.
A Neural Framework for Generalized Causal Sensitivity Analysis
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
In this paper, we propose a new, representation-agnostic refutation framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation.
Counterfactual Fairness for Predictions using Generative Adversarial Networks
It is often achieved through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute.
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Treatment effect estimation in continuous time is crucial for personalized medicine.
Analyzing User Characteristics of Hate Speech Spreaders on Social Media
Resharing is an important driver behind the spread of hate speech on social media.
Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models
Document-level relation extraction aims at inferring structured human knowledge from textual documents.
DSG: An End-to-End Document Structure Generator
To the best of our knowledge, our DSG system is the first end-to-end trainable system for hierarchical document parsing.
Generative AI
Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research.
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine
The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero.
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Here, we compare short-term probabilistic forecasts of popular mechanistic models based on the renewal equation with forecasts of statistical time series models.
HQP: A Human-Annotated Dataset for Detecting Online Propaganda
(2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64. 03).
Addressing distributional shifts in operations management: The case of order fulfillment in customized production
We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard.
Fair Off-Policy Learning from Observational Data
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons.
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Unlike existing approaches, we generate pseudo labels and propose to train the model via a novel attention-based contrastive adaptation method.
Normalizing Flows for Interventional Density Estimation
To the best of our knowledge, our Interventional Normalizing Flows are the first proper fully-parametric, deep learning method for density estimation of potential outcomes.
Estimating individual treatment effects under unobserved confounding using binary instruments
(2)~We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for CATE estimation, in the sense that it achieves a faster rate of convergence if the CATE is smoother than the individual outcome surfaces.
Locating disparities in machine learning
As a result, ALD helps practitioners and researchers of algorithmic fairness to detect disparities in machine learning algorithms, so that disparate -- or even unfair -- outcomes can be mitigated.
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model
In this work, we develop a duration-dependent hidden Markov model.
Algorithmic Fairness in Business Analytics: Directions for Research and Practice
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies.
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
To the best of our knowledge, ours is the first framework to learn domain-invariant, contextual representation for UDA of time series data.
Causal Transformer for Estimating Counterfactual Outcomes
In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.
Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine
Common methods for learning optimal DTRs, however, have shortcomings: they are typically based on outcome prediction and not treatment effect estimation, or they use linear models that are restrictive for patient data from modern electronic health records.
Web Mining to Inform Locations of Charging Stations for Electric Vehicles
To address our research question, we propose the use of web mining: we characterize the influence of different POIs from OpenStreetMap on the utilization of charging stations.
Interpretable Off-Policy Learning via Hyperbox Search
Using a simulation study, we demonstrate that our algorithm outperforms state-of-the-art methods from interpretable off-policy learning in terms of regret.
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention.
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies
To this end, we develop the Deconfounding Temporal Autoencoder, a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders.
Generalizing Off-Policy Learning under Sample Selection Bias
Since training data is often not representative of the target population, standard policy learning methods may yield policies that do not generalize target population.
A Sociotechnical View of Algorithmic Fairness
We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles.
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective.
Sequential Deconfounding for Causal Inference with Unobserved Confounders
In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders.
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
Directed particle swarm optimization with Gaussian-process-based function forecasting
Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths.
Estimating Average Treatment Effects via Orthogonal Regularization
In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness.
Monitoring the COVID-19 epidemic with nationwide telecommunication data
We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; February 10 - April 26, 2020), consisting of ~1. 5 billion trips.
Applications
Estimating Treatment Effects via Orthogonal Regularization
Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT) which learn outcomes that are orthogonal to the treatment assignment.
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents
As this approach can incorporate any active learning agent into its ensemble, it allows to increase the performance of every active learning agent by learning how to combine it with others.
IntKB: A Verifiable Interactive Framework for Knowledge Base Completion
(ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero- and few-shot relations over time.
Estimating Risk-Adjusted Hospital Performance
These findings demonstrate that a large portion of the variance in health outcomes can be attributed to non-linear relationships between patient risk variables and implicate that the current approach of measuring hospital performance should be expanded.
Learning a Cost-Effective Annotation Policy for Question Answering
State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive.
Practical Annotation Strategies for Question Answering Datasets
Here, remarkably, annotating a stratified subset with only 1. 2% of the original training set achieves 97. 7% of the performance as if the complete dataset was annotated.
DocParser: Hierarchical Structure Parsing of Document Renderings
Translating renderings (e. g. PDFs, scans) into hierarchical document structures is extensively demanded in the daily routines of many real-world applications.
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning
The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV).
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.
RankQA: Neural Question Answering with Answer Re-Ranking
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer.
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach
For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level.
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers.
Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems
As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs.
Reinforcement Learning in R
Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error.
Adaptive Document Retrieval for Deep Question Answering
State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer.
News-based trading strategies
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets.
Deep learning in business analytics and operations research: Models, applications and managerial implications
(4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning.
Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics.
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein.
Deep learning for affective computing: text-based emotion recognition in decision support
Emotions widely affect human decision-making.
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions
The macroeconomic climate influences operations with regard to, e. g., raw material prices, financing, supply chain utilization and demand quotas.
Decision support from financial disclosures with deep neural networks and transfer learning
Hence, this paper studies the use of deep neural networks for financial decision support.
Statistical Inferences for Polarity Identification in Natural Language
Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives.
Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials.
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees
To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree.
Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures
Decision analytics commonly focuses on the text mining of financial news sources in order to provide managerial decision support and to predict stock market movements.
