causal inference
Introduction to Causal Inference
... these causal inference problems, particularly in the area of graphical causal ...represent causal relations (for example, directed acyclic graphs); well defined connec- tions between aspects ...
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A weight of evidence approach to causal inference
... In a weight of evidence approach to causal inference, two aspects need to be quantified. First, the probability that a certain criterion is met needs to be estimated based on the available (epidemiological ...
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Causal Inference Beyond Estimating Average Treatment Effects
... in causal inference. We elucidated the causal definition of the MAFF using the potential outcome framework and developed a method to estimate the MAFF from the ...
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Essays in Cluster Sampling and Causal Inference
... sampling, causal inference, and measurement ...corresponding inference has been predominantly ...implement inference for the unknown cluster sizes simultaneously with inference for ...
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Causal Inference in Discretely Observed Continuous Time Processes
... In causal inference for longitudinal data, standard methods usually assume that the underlying processes are discrete time processes, and that the observational time points are the time points when the ...
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Causal Inference through a Witness Protection Program
... in causal inference is the estimation of a causal effect when treatment and outcome are ...estimating causal effects that exploits observational conditional independencies to suggest “weak” ...
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Causal Inference with Covariate Balance Optimization
... Causal inference is pervasive in many ...of causal effects by comparing the outcome when an action is applied with the outcome when no action is ...the causal diagram for a nonrandomized study ...
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Misunderstandings between experimentalists and observationalists about causal inference
... Summary. We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and ...
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Causal inference and interpretable machine learning for personalised medicine
... rather causal in nature (Pearl et ...as causal inference or discovery ...possible. Causal discovery questions are generally challenging and cannot be answered solely on the basis of data, ...
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Essays on causal inference and political representation
... the causal inference techniques that can be used to measure these quantities of ...using causal inference techniques to more efficiently estimate quantities of interest in questions of ...
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Flexible causal inference for political science
... in causal inference have been forced to choose which unpalatable assumptions they wished to embrace in the face of endogeneity issues: they could utilize a potentially weak or invalid instrument, assume ...
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Extended conditional independence and applications in causal inference
... The layout of the paper is as follows. In Section 2 we give the definition of a sep- aroid, an algebraic structure with five axioms, and show that stochastic conditional independence and variation conditional ...
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Bayesian Nonparametric Methods For Causal Inference And Prediction
... covariates and marginals for the covariates. The EDP prior that is placed on the regression parameters and the parameters on the covariates induces clustering among subjects determined by similarity in their regression ...
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Non-linear Causal Inference using Gaussianity Measures
... Most of the methods introduced in this section assume some form of noise in the gen- erative process of the effect. Thus, their use is not justified in the case of noiseless data. Janzing et al. (2012) describe a method ...
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Machine Learning for causal Inference on Observational Data
... randomized trial method has not been applied but it is still important to try to de- termine causes and effects from that data. This is the case in most organizations in the present since they would possibly be ...
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Bayesian Methods for Optimal Treatment Allocation and Causal Inference.
... frequentist inference in various applications ...for causal inference because many full sample estimators are available for estimating average causal effects (ACE) such as the outcome ...
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Feature Selection as Causal Inference: Experiments with Text Classification
... liably learn if a feature has a significant, causal effect on document classes. While the concept of causality does not apply to document classification as naturally as in other tasks, the methods used for ...
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Causal Inference Using Variation In Treatment Over Time
... in causal inference from complex longitudinal data, which play a prominent role in public health, economics, and epidemiology, as well as in biological and medical ...the causal effect of certain ...
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Statistical Analysis in Empirical Bayes and in Causal inference
... titled Causal Inference Analysis, we study the estimation of the causal effect of treatment on survival probability up to a given time point among those subjects who would comply with the assignment ...
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Challenges of Using Text Classifiers for Causal Inference
... but causal inference from observational data has typically only been applied to structured, low-dimensional ...in causal inference has not previously been ...facilitate causal analyses ...
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