Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger caus…
Study uses causal machine learning to assess coupon campaign impact on retailer sales.
problem Assessing the causal effect of a coupon campaign on retailer sales.
method Causal machine learning algorithms, subgroup analysis, optimal policy learning.
result Only two coupon categories (drugstore and other food) have a significant positive impact on sales.
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…
This paper improves credit line impact analysis by considering spending as a distribution.
problem Previous studies on credit lines' impact on spending have overlooked the distributional nature of spending.
method Developed a distribution-valued estimator framework to extend existing real-valued estimators.
result Credit lines positively influence spending across all quantiles, but more towards luxuries as they increase.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
BaMANI uses ensemble learning to improve Bayesian network inference.
problem Bayesian network inference's reliance on specific algorithms can obscure causal relationships.
method Developed an ensemble learning approach to marginalize algorithm impact.
result Improved accuracy and reliability of causal network predictions.
Framework for discovering treatment benefits in user segments.
problem Discovering differential impacts of treatments across user subgroups.
method Combines causal inference and machine learning for user segment discovery.
result Unified approach for treatment benefit discovery and assignment.
Bayesian Causal Forests model assesses part-time work's impact on student growth.
problem Estimating causal effects of part-time work on student growth in mathematics achievement.
method Longitudinal Bayesian Causal Forests model combining non-parametric and difference-in-differences methods.
result Negative impact of part-time work for most students, potential benefits for those with low school belonging, widening achievement gap identified.
Study evaluates how noise affects ANMs' ability to identify causal directions.
problem Challenges in identifying causal relationships in bivariate cases with noise.
method Empirical study using Regression with Subsequent Independence Test (RESIT) on various ANM models.
result ANMs can fail to identify true causal directions for certain noise levels.
Study uses ML and causal analysis to predict student performance factors.
problem Understanding socio-academic and economic factors affecting student performance.
method Employed machine learning techniques and causal analysis on 1,050 student profiles.
result Ridge Regression achieved robust predictions with MAE of 0.12 and MSE of 0.024.
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explic…
New framework for estimating treatment effects in experiments with network interference.
problem Network interference biases traditional treatment effect estimations in randomized experiments.
method Causal message-passing framework based on high-dimensional approximate message passing.
result Practical algorithm to estimate total treatment effect in multi-period experiments.
CEILS generates feasible counterfactual explanations by considering causal impacts.
problem Current counterfactual explanations lack feasibility and causal impact consideration.
method CEILS integrates causal reasoning into existing counterfactuals generation algorithms.
result CEILS provides feasible recommendations to achieve desired outcomes.
ACI identifies cause-effect relationships and causal influence ranges in dynamical systems.
problem Detecting and quantifying causal influence ranges in complex systems.
method Bayesian data assimilation and assimilative causal inference (ACI) to trace causes back from observed effects.
result Mathematically rigorous formulations of forward and backward causal influence ranges (CIRs) for nonlinear dynamical systems.
Study finds racial bias in pulse oximeter readings has minimal impact on ICU ventilation rates.
problem Racial disparities in pulse oximeter readings affect clinical decisions in ICU settings.
method Causal inference using path-specific effects and doubly robust estimator.
result Minimal impact of racial discrepancies on invasive ventilation rates, but more pronounced on ventilation duration.
New method quantifies variable importance in causal forests for treatment effect heterogeneity.
problem Lack of understanding how input variables affect treatment effect heterogeneity in causal forests.
method Developed a new importance variable algorithm for causal forests based on the drop and relearn principle.
result Shows how to handle forest retraining without a confounding variable and introduces a corrective term for confounders.
Microscopic (pore-scale) properties of porous media affect and often determine their macroscopic (continuum- or Darcy-scale) counterparts. Understanding the relationship between processes on these two scales is essential to both the derivation of macroscopic models of, e.g., transport phenomena in natural porous media,…
Transformer-based method for causal discovery with prior knowledge integration.
problem Complex nonlinear dependencies and spurious correlations in time series data.
method Multi-layer Transformer forecaster with gradient-based causal structure extraction and attention masking for prior knowledge integration.
result Significant improvement in causal discovery and causal lag estimation compared to state-of-the-art methods.
Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.
problem Sparse data hinders policy development and evaluation.
method Satellite imagery, machine learning, and ML inference techniques.
result Grid access improves rural asset wealth by 0.17 standard deviations.
New insights show Medicaid impacts on ED use vary widely, with some groups seeing significant increases.
problem Understanding the varied impacts of Medicaid on emergency department use.
method Causal machine learning methods to identify heterogeneous impacts.
result Meaningful heterogeneity in the effect of Medicaid on ED use, with a small group driving the overall effect.
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
problem Impact of economic policies on wealth distribution in token economies.
method Eliminated noise in wealth distribution data using macroeconomic and microeconomic time series. Causality analysis between BIPs and wealth distribution data.
result Proposed a structure for economic policy taxonomy in token economies.
DeepCausalMMM models marketing impacts using deep learning and causal inference.
problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.
method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.
result Captures non-linear dynamics and temporal patterns in marketing impacts.
This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
This paper uses machine learning to estimate how different types of crashes affect highway traffic.
problem Estimating the heterogeneous causal effects of crashes on highway traffic.
method Neyman-Rubin Causal Model, Conditional Shapley Value Index, Structural Causal Model, Doubly Robust Learning.
result Different types of crashes have varying impacts on traffic, with rear-end crashes causing the most severe congestion.
Estimates treatment effects in rare extreme events using EVT.
problem Estimating treatment effects in rare, impactful events like extreme climate events.
method Introduces a novel framework using EVT and multivariate regular variation for consistent treatment effect estimation.
result Developed a consistent estimator for extreme treatment effects with rigorous non-asymptotic analysis.
New causal analysis reconciles predictive and statistical fairness.
problem Mutual exclusivity of predictive and statistical fairness notions.
method Derive a new causal decomposition formula for fairness measures.
result Predictive and statistical fairness are complementary, not mutually exclusive.
The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations b…
New model analyzes customer churn with tensor completion and binary data.
problem Analyzing the impact of interventions on customer churn.
method Tensorized latent factor block hazard model with 1-bit tensor completion.
result Effective categorization of interventions by similar impacts.
The paper examines how timing of observations affects causal discovery methods.
problem The sensitivity of causal discovery methods to mismatched observation timing.
method Empirical and theoretical analysis of classical and recent causal discovery methods.
result Causal discovery methods are sensitive to sampling rate and window length.
A new concept of causality for abstract phenomena.
problem Unclear definition of causality in real-life variables.
method Introduces 'phenomenological causality' based on elementary actions.
result Defines causal structure without hard-wired links.
Framework identifies causal direction from single data setting.
problem Identify causal direction from single observational data.
method VCEI framework based on ICM principle and artificial variation.
result VCEI is competitive to other frameworks in identifying causal direction.
This paper identifies and bounds ICE central moments using PO marginal central moments.
problem Identifying and characterizing treatment effect heterogeneity.
method Using only marginal central moments of potential outcomes, the paper identifies and bounds central moments of individual causal effects.
result Identification and bounding of central moments of ICE using marginal moments of POs.
Proposes a framework for causal inference with processed outcomes in biomedical research.
problem Impact of intra-subject processing on inter-subject statistical inference in biomedical research.
method Semiparametric framework with multiply robust estimators and step-down procedure for high-dimensional inference.
result Superior performance of the proposed approach demonstrated through simulations and application to autism research.
Introduces causal fairness analysis to address unfairness in AI decisions.
problem Fairness issues in AI decision-making.
method Link observed disparities to underlying causal mechanisms.
result Develops Fairness Map and Fairness Cookbook.
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.
Debias concept-based explanations by removing confounding information.
problem Correlation between concepts and confounding features.
method Causal prior graph and two-stage regression technique.
result Success in removing biases and improving concept ranking.
FairTrade uses variational inference to create fair predictions in causal models.
problem Creating fair predictions in machine learning models with causal reasoning.
method FairTrade uses variational inference to account for unobserved confounders and integrates fairness constraints on causal paths.
result Demonstrates the effectiveness of FairTrade in creating fair predictions in both simulated and real-world datasets.
Collectivistic countries influence Bitcoin returns more than individualistic ones during the pandemic.
problem The impact of public attention to COVID-19 on Bitcoin returns.
method Rolling and recursive-evolving algorithms to account for timing and estimation bias.
result Collectivistic countries have stronger causal impacts on Bitcoin returns.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
problem Aggregated variables can mask causal effects, leading to paradoxical confounding.
method Analysis of how aggregated variables can change the definition of causality and the feasibility of causal relations.
result Macro causal relations are defined by micro states, not just aggregated variables.
Paper proposes learning causal graphs with only relevant variables.
problem Discovering causal relationships in large-scale graphs often includes irrelevant variables.
method Developed NSCSL algorithm to learn necessary and sufficient causal graphs (NSCG).
result NSCSL algorithm identifies relevant causal features for specific outcomes.
Granger-causality in the frequency domain is an emerging tool to analyze the causal relationship between two time series. We propose a bootstrap test on unconditional and conditional Granger-causality spectra, as well as on their difference, to catch particularly prominent causality cycles in relative terms. In particu…
Synthetic experiments are crucial for assessing causal machine learning methods.
problem Current empirical evaluations of causal machine learning methods are insufficient and unreliable.
method Propose principles for conducting rigorous empirical analyses with synthetic data.
result Rigorous synthetic experiments are essential for building trust in causal machine learning methods.
Proposes LLM-DCD for improved causal discovery from data.
problem Challenges in discovering causal relationships from observational data.
method Uses LLM to initialize DCD optimization, incorporating priors.
result Higher accuracy on benchmark datasets compared to state-of-the-art.
CausalBench aims to advance causal learning research with a transparent platform.
problem Lack of unified benchmark datasets, algorithms, metrics, and evaluation interfaces for causal learning.
method Introduces CausalBench, a flexible benchmark framework for causal analysis and machine learning.
result Promotes scientific collaboration, reproducibility, and awareness in causal learning research.
New framework infers causal shifts in event sequences under out-of-domain interventions.
problem Inferring causal relationships in event sequences without considering out-of-domain interventions.
method Proposes a new causal framework to define ATE, designs an unbiased ATE estimator, and uses a Transformer-based neural network model.
result Demonstrates superior performance in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.
Study evaluates the impact of academic support center's face-to-face assistance on student performance.
problem Underestimation of Academic Support Center's true impact due to group bias.
method Applied causal inference theory and T-learner to evaluate conditional average treatment effect (CATE) of F2F personal assistance.
result Developed a new CATE function that depends on the number of F2F sessions, predicting improved CATE performance.
AR CI framework handles complex confounders and sequential actions.
problem Low-dimensional confounders and singleton actions in causal inference.
method Sequencification to transform data into sequences, enabling CI with complex confounders and sequential actions.
result AR model can estimate multiple causal quantities using a single model, simplifying inference and improving outcome prediction.