scholarly journals Gaussian prepivoting for finite population causal inference

2021 ◽  
Author(s):  
Peter L. Cohen ◽  
Colin B. Fogarty
Keyword(s):  
Causal Inference ◽  
Finite Population

Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments

2018 ◽  
Vol 113 (522) ◽  
pp. 868-881 ◽  
Author(s):  
Rahul Mukerjee ◽  
Tirthankar Dasgupta ◽  
Donald B. Rubin
Keyword(s):  
Causal Inference ◽  
Finite Population ◽  
Finite Population Sampling ◽  
Population Sampling

scholarly journals Bridging Finite and Super Population Causal Inference

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Peng Ding ◽  
Xinran Li ◽  
Luke W. Miratrix
Keyword(s):  
Causal Inference ◽  
Finite Population ◽  
Causal Effect ◽  
Causal Analysis ◽  
Potential Outcomes ◽  
Alternative Formulation ◽  
Randomized Experiments ◽  
Infinite Population ◽  
Variance Estimators ◽  
Average Causal Effect

AbstractThere are two general views in causal analysis of experimental data: the super population view that the units are an independent sample from some hypothetical infinite population, and the finite population view that the potential outcomes of the experimental units are fixed and the randomness comes solely from the treatment assignment. These two views differs conceptually and mathematically, resulting in different sampling variances of the usual difference-in-means estimator of the average causal effect. Practically, however, these two views result in identical variance estimators. By recalling a variance decomposition and exploiting a completeness-type argument, we establish a connection between these two views in completely randomized experiments. This alternative formulation could serve as a template for bridging finite and super population causal inference in other scenarios.

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

Causal Inference and Mediation: Cross-sectional Biases of Longitudinal Processes

2006 ◽  
Author(s):  
Jonathan N. Frecceri ◽  
Scott E. Maxwell
Keyword(s):  
Causal Inference ◽  
Cross Sectional

A Causal Inference Explanation for Multisensory Speech Perception

2013 ◽  
Author(s):  
John F. Magnotti ◽  
Wei Ji Ma ◽  
Michael S. Beauchamp
Keyword(s):  
Speech Perception ◽  
Causal Inference

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Causal Inference in Industrial Alarm Data by Timely Clustered Alarms and Transfer Entropy

2020 ◽  
Author(s):  
Mina Fahimipirehgalin ◽  
Iris Weiss ◽  
Birgit Vogel-Heuser
Keyword(s):  
Causal Inference ◽  
Transfer Entropy

Causal Inference and Assessing Learning Outcomes Across Multiple Methods: A Primer for International Evaluators in Education

2018 ◽  
Vol 10 (1) ◽  
pp. 219-234
Author(s):  
John H. Hitchcock ◽  
◽  
Anthony J. Onwuegbuzie ◽  
Shannon David ◽  
Anne-Maree Ruddy ◽  
...  
Keyword(s):  
Causal Inference ◽  
Learning Outcomes ◽  
Multiple Methods
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