Papers Causal Identification
“Causal Identification” 태그가 달린 논문 48편 · 필터 해제
Comment: Reflections on the Deconfounder
The aim of this comment (set to appear in a formal discussion in JASA) is to draw out some conclusions from an extended back-and-forth I have had with Wang and Blei regarding the deconfounder method proposed in "The Bles…
Causal IdentificationMeasuring causal influence with back-to-back regression: the linear case
Identifying causes from observations can be particularly challenging when i) potential factors are difficult to manipulate individually and ii) observations are complex and multi-dimensional. To address this issue, we in…
Causal IdentificationregressionHuman detection of machine manipulated media
Recent advances in neural networks for content generation enable artificial intelligence (AI) models to generate high-quality media manipulations. Here we report on a randomized experiment designed to study the effect of…
Causal IdentificationHuman DetectionDetection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator
We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock ma…
Causal IdentificationAdapting Text Embeddings for Causal Inference
Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post with the author's gender affect the post popularity? This paper develops a method to estimate such causal effects from observational …
Causal IdentificationCausal InferenceDimensionality ReductionLanguage Modeling+4Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the gener…
Causal IdentificationCausal InferenceSelection biasCausal Identification under Markov Equivalence
Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences. The problem of identification of causal effects is concerned with determining whether a caus…
Causal IdentificationCausality Refined Diagnostic Prediction
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we…
Causal IdentificationDecision MakingDiagnosticPrediction