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Branch of statistics
larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable
Causal_inference
Method of statistical analysis
the "fundamental problem of causal inference." Because of the fundamental problem of causal inference, unit-level causal effects cannot be directly observed
Rubin_causal_model
Development of artificial intelligence
Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation
Causal_AI
Conceptual model in philosophy of science
among variables and to guide inference. By clarifying which variables should be included, excluded, or controlled for, causal models can improve the design
Causal_model
Method of identifying the fundamental causes of faults or problems
epidemiology (e.g., to identify the source of an infectious disease), where causal inference methods often require both clinical and statistical expertise to make
Root-cause_analysis
Probabilistic graphical representation of causal relationships
Mathematics portal Bayesian epistemology Bayesian programming Causal inference Causal loop diagram Chow–Liu tree Computational intelligence Computational
Bayesian_network
Process of identifying causality
cause and effect occurred in Aristotle's Physics. Causal inference is an example of causal reasoning. Causal relationships may be understood as a transfer
Causal_reasoning
2000 book by Judea Pearl
on causal inference in several fields including statistics, computer science and epidemiology. In this book, Pearl espouses the Structural Causal Model
Causality_(book)
Field in statistics pertaining to establishing cause and effect
require different techniques for causal inference (because, for example, of issues such as confounding). Causal inference techniques used with experimental
Exploratory_causal_analysis
Field of statistics
require different techniques for causal inference (because, for example, of issues such as confounding). Causal inference techniques used with experimental
Causal_analysis
Method of logical reasoning
generalization, prediction, statistical syllogism, argument from analogy, and causal inference. There are also differences in how their results are regarded. A generalization
Inductive_reasoning
2018 book by Judea Pearl and Dana Mackenzie
writer Dana Mackenzie. The book explores the subject of causality and causal inference from statistical and philosophical points of view for a general audience
The_Book_of_Why
How one process influences another
Maziarz, Mariusz (2020). The Philosophy of Causality in Economics: Causal Inferences and Policy Proposals. New York & London: Routledge. Born, M. (1949)
Causality
Refutation of a logical fallacy
scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning. Causal inference is widely studied
Correlation does not imply causation
Correlation_does_not_imply_causation
Theoretical framework
Collapsibility in Causal Inference". Statistical Science. 14 (1): 29–46. doi:10.1214/ss/1009211805. Hernán, M.A.; Robins, J.M. (2020). "Causal Inference: What If"
Conceptual_model
Empirical statistical testing of economic theories
doi:10.2307/2979889. ISSN 0952-8385. "4 Potential Outcomes Causal Inference – Causal Inference The Mixtape". mixtape.scunning.com. Retrieved 26 October
Econometrics
Study of health and disease within a population
has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude
Epidemiology
Directed graph that models causal relationships between variables
Causal graphs can be used for communication and for inference. They are complementary to other forms of causal reasoning, for instance using causal equality
Causal_graph
In-depth, detailed examination of a particular case
certain standard causal identification problems." By using Bayesian probability, it may be possible to make strong causal inferences from a small sliver
Case_study
American-Canadian scientist
Political Methodology. Sekhon's primary research interests lie in causal inference, machine learning, and their intersection. He has also published research
Jasjeet_S._Sekhon
American epidemiologist
considerations in drug safety research. He has also co-authored work on causal inference in public health. Myrto Lefkopoulou Distinguished Lecturer, Harvard
Steven_N._Goodman
Error in statistical reasoning with groups
frequency data are unduly given causal interpretations. The paradox can be resolved when confounding variables and causal relations are appropriately addressed
Simpson's_paradox
Bias in causal inference
In causal inference, confounding is a form of systematic error (or bias) that can distort estimates of causal effects in observational studies. A confounder
Confounding
American epidemiologist
and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly
James_Robins
American computer scientist (born 1936)
propagation). He is also credited for developing a theory of causal and counterfactual inference based on structural models (see article on causality). In
Judea_Pearl
Criteria for measuring cause and effect
epidemiology is the best chances of avoiding bias and accidental findings in causal inference evaluations, being the use of big data sets of raw non-selective long-term
Bradford_Hill_criteria
American economist
evaluation methods (causal inference) and making it more accessible to practitioners. He wrote the Yale University Press textbook Causal Inference: The Mixtape
Scott_Cunningham_(economist)
American epidemiologist
analysis along with the book, Explanation in Causal Inference, on the topic. His work on causal inference is grounded in the potential outcomes framework
Tyler_VanderWeele
Reason for or origin of a disease or pathology
lines of evidence together are required to for causal inference. Austin Bradford Hill demonstrated a causal relationship between tobacco smoking and lung
Cause_(medicine)
Dutch-American econometrician
modifications to random forests called causal forests, to estimate heterogeneous treatment effects in causal inference models. Imbens received the 2021 Nobel
Guido_Imbens
Scientific procedure performed to validate a hypothesis
ISBN 978-981-256-649-2. Holland, Paul W. (December 1986). "Statistics and Causal Inference". Journal of the American Statistical Association. 81 (396): 945–960
Experiment
1994 book written by Gary King, Robert Keohane, and Sidney Verba
of inference." The book primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference
Designing_Social_Inquiry
Statistical research award
Leuven, presented by King Philippe of Belgium. The awarded topic was Causal Inference with application in Medicine and Public Health, with laureates James
Rousseeuw Prize for Statistics
Rousseeuw_Prize_for_Statistics
Process of using data analysis for predicting population data from sample data
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis
Statistical_inference
Statistical matching technique
Causal Inference". Political Analysis. 15 (3): 199–236. doi:10.1093/pan/mpl013. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference"
Propensity_score_matching
or no omitted variable bias. This idea is part of the Neyman-Rubin Causal Inference Model, developed by Jerzy Neyman in the 1920s, and later extended by
Ignorability
American statistician
from 1986 through 2021.[1] [2][3] He has written extensively about causal inference in observational studies, including sensitivity analysis, optimal matching
Paul_R._Rosenbaum
Approach to program evaluation
to operationalize contextual, process, and outcome variables; provides a causal explanation of how and why outcomes were achieved, including whether the
Theory-driven_evaluation
American political scientist (born 1942)
of Collier's central contributions concerns qualitative tools for causal inference. Here, central thrusts of Collier's work have been to put ideas about
David Collier (political scientist)
David_Collier_(political_scientist)
American statistician
2015-03-15 at the Wayback Machine The Monkey Cage Statistical Modeling, Causal Inference, and Social Science: https://statmodeling.stat.columbia.edu/ Archived
Andrew_Gelman
Theory in psychology
1971, 1972, 1973) is an attribution theory in which people make causal inferences to explain why other people and ourselves behave in a certain way
Covariation_model
Interdisciplinary research discipline
method. Other methods, such as causal machine learning and causal tree, provide distinct advantages, including inference testing. There are notable advantages
Computational_economics
techniques that seek to test or provide evidence for specific kinds of causal relationships in biological systems. A necessary cause is one without which
Biological tests of necessity and sufficiency
Biological_tests_of_necessity_and_sufficiency
Deviations from local realism
Fritz, T (2019). "The Inflation Technique for Causal Inference with Latent Variables". Causal Inference. 7 (2) 20170020. arXiv:1609.00672. doi:10.1515/jci-2017-0020
Quantum_nonlocality
Statistical hypothesis test for forecasting
ISBN 978-0-324-35904-6. Leamer, Edward E. (1985). "Vector Autoregressions for Causal Inference?". Carnegie-Rochester Conference Series on Public Policy. 22: 283.
Granger_causality
Method to develop and test theories
is generally understood as a "within-case" method to draw inferences on the basis of causal mechanisms, but it can also be used for ideographic research
Process_tracing
Statistical method
The Kitagawa–Oaxaca–Blinder (KOB) decomposition, or simply Kitagawa decomposition or Blinder–Oaxaca decomposition (/ˈblaɪndər wɑːˈhɑːkɑː/), is a statistical
Kitagawa–Oaxaca–Blinder decomposition
Kitagawa–Oaxaca–Blinder_decomposition
Argentine biostatistician
Rotnitzky is an Argentine biostatistician whose research involves causal inference on the effects of medical interventions in the face of missing data
Andrea_Rotnitzky
1739–40 book by David Hume
experience when the mind (conditioned by repeated observation) makes a causal inference. And though his conclusion is shocking to common sense, Hume explains
A_Treatise_of_Human_Nature
Study of senses and nervous system
to make causal inference of sensory signals. The difference between two models is that hierarchical model can explicitly make causal inference to predict
Multisensory_integration
Field of study
research challenges in social epidemiology include tools to strengthen causal inference, methods to test theories of illness such as fundamental cause theory
Social_epidemiology
Statistical method in genetic epidemiology
path analysis, a form of causal diagram used for making causal inference from non-experimental data. The method relies on causal anchors, and the anchors
Mendelian_randomization
Subset of variables that contains all the useful information
blanket or boundary allows for efficient inference and helps isolate relevant variables for prediction or causal reasoning. The terms Markov blanket and
Markov_blanket
Statistical estimation framework for causal inference
Loss-Based Estimation) is a general statistical estimation framework for causal inference and semiparametric models. TMLE combines ideas from maximum likelihood
Targeted maximum likelihood estimation
Targeted_maximum_likelihood_estimation
Competitive algorithm for searching a problem space
performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. In a genetic algorithm, a population of candidate solutions (called
Genetic_algorithm
Mathematical framework for identifying causal effects
{\displaystyle Z} . Do-calculus can be applied to various domains within causal inference such as mediation analysis in decomposing direct and indirect effects
Do-calculus
scholarship in the social sciences that links, usually through quantitative causal inference, historical events with later political, economic and social outcomes
Persistence_studies
Event that is closest to, or immediately responsible for causing, some observed result
cause, which acts less directly through the proximate cause. In formal causal inference, the proximate cause is called a mediator. Example: Why did the ship
Proximate and ultimate causation
Proximate_and_ultimate_causation
Bias which mixes cause and effect
reverse causation. Establishing temporality is a major criterion for causal inference. This bias is common in observational research, such as cross-sectional
Temporal_bias
Statistical modeling technique
\tau )} so that β τ {\displaystyle \beta _{\tau }} can be used for causal inference. Specifically, the hypothesis H 0 : ∇ f ( x , τ ) = 0 {\displaystyle
Quantile_regression
German computer scientist
independence testing. Starting in 2005, Schölkopf turned his attention to causal inference. Causal mechanisms in the world give rise to statistical dependencies as
Bernhard_Schölkopf
Process by which a disease or disorder develops
the area of causal inference. If the pathogenesis of a condition is not known, it is considered to be an idiopathic disease. Causal inference Epidemiology
Pathogenesis
Four criteria showing a causal relationship between a causative microbe and a disease
results of host mutations.[clarification needed] Bradford Hill criteria Causal inference Molecular Koch's postulates The fourth postulate was added later Koch
Koch's_postulates
American statistician
Philadelphia. He is most well known for the Rubin causal model, a set of methods designed for causal inference with observational data, and for his methods
Donald_Rubin
Bias in a statistical analysis due to non-random selection
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that the association between exposure
Selection_bias
Data analysis technique
implicants or descriptive inferences derived from the data by the QCA method are causal requires establishing the existence of causal mechanism using another
Qualitative comparative analysis
Qualitative_comparative_analysis
Scottish philosopher, historian, economist and essayist (1711–1776)
conjunction" of events. This problem of induction means that to draw any causal inferences from past experience, it is necessary to presuppose that the future
David_Hume
Process involving chance used in research for allocating experimental subjects to groups
can be used to adapt the inference to the sampling method. Randomization was emphasized in the theory of statistical inference of Charles S. Peirce in
Random_assignment
Statistical technique to use observational data for causal analysis
trends, but when carefully designed, they provide policymakers with robust causal estimates using observational data. Difference-in-differences has also been
Difference_in_differences
Movement in empirical economics
estimation through the average treatment effect (ATE) is able to reframe causal inference as a missing data problem, and evaluate the difference between observable
Credibility_revolution
Dutch statistician
and causal inference. He also developed the targeted maximum likelihood estimation methodology. He is a founding editor of the Journal of Causal Inference
Mark_van_der_Laan
Concept in econometrics
overlooked in non-experimental research, which limits the validity of causal inference and the ability to draw reliable policy recommendations. Common solutions
Endogeneity_(econometrics)
Statistical method
environments where random assignment to conditions is unfeasible. True causal inference using RDDs is still impossible, because the RDD cannot account for
Regression discontinuity design
Regression_discontinuity_design
Metrics linking marketing actions to outcomes
Return on marketing investment Customer acquisition cost A/B testing Causal inference "Marketing Accountability". Marketing Accountability Standards Board
Marketing_accountability
Incentive with unintended results
equilibrium Empirical methods Experimental Econometrics Time series Spatial Causal inference Quasi-experiments Prescriptive and policy Welfare analysis Social choice
Perverse_incentive
Topics referred to by the same term
validity of causal inferences within scientific studies, usually based on experiments External validity, the validity of generalized causal inferences in scientific
Validity
Ability to make choices voluntarily
behaviors so as to conform to or violate the two requirements for causal inference. Through such work, Wegner has been able to show that people often
Free_will
Estimation of the impact of marketing tactics on sales
Marketing mix modeling (MMM) is a statistical causal inference and forecasting methodology used to estimate the impact of various marketing tactics on
Marketing_mix_modeling
direct effect of treatment. One solution to this problem is to redefine the causal estimand of interest by redefining a subject's potential outcomes in terms
Spillover_(experiment)
Statistical model
assessed separately. Causal inference Latent variable As of 19 June 2014, this article is derived in whole or in part from Causal Analysis in Theory and
Mediation_(statistics)
Extent to which the results of a study can be generalized
by its internal validity. If a causal inference made within a study is invalid, then generalizations of that inference to other contexts will also be
External_validity
Statistical models in epidemiology
Marginal structural models are a class of statistical models used for causal inference in epidemiology. Such models handle the issue of time-dependent confounding
Marginal_structural_model
Type of statistical data method
In causal inference, synthetic controls are a class of methods where the quasi-experimental control group is synthesized from a weighted average of potential
Synthetic_control_method
Variable that is causally influenced by two or more variables
In statistics and causal graphs, a variable is a collider when it is causally influenced by two or more variables. The name "collider" reflects the fact
Collider_(statistics)
Statistical term
causal modeling and analysis of covariance structures. Path analysis is considered by Judea Pearl to be a direct ancestor to the techniques of causal
Path_analysis_(statistics)
Best practices for running elections
North Carolina is no longer a democracy ..." Statistical Modeling, Causal Inference, and Social Science. Andrew Gelman. Retrieved 11 June 2022. Gelman
Electoral_integrity
American statistician and epidemiologist
contributions to statistical and epidemiologic methods including Bayesian and causal inference, bias analysis, and meta-analysis. His focus has been the extensions
Sander_Greenland
difference in differences and other quasi-experimental methods used for causal inference. In 2009, Sant'Anna received his bachelor's degree in economics from
Pedro_H.C._Sant'Anna
American psychologist
known for her work on the early childhood development of cognition, causal inference, discovery, and learning. Schulz received a Bachelor of Arts with a
Laura_Schulz
Italian statistician
of biostatistics at Harvard University. She develops methodology in causal inference and data science and leads research projects that combine big data
Francesca_Dominici
2005 essay written by John Ioannidis
we learned since 2004 (or 1984, or 1964)? « Statistical Modeling, Causal Inference, and Social Science". statmodeling.stat.columbia.edu. Retrieved 2020-03-28
Why Most Published Research Findings Are False
Why_Most_Published_Research_Findings_Are_False
Practise of standing in a powerful way
creates an asymmetric demand effect, which precludes making correct causal inference). Since its promotion in a 2010 Harvard Business School Working Knowledge
Power_posing
Influence of pornography on an individual and their intimate relationships
(2002). Experimental and Quasi-Experimental Designs for Generilized Causal Inference Boston:Houghton Mifflin. Levine, G. and Parkinson, S. (1994). Experimental
Effects_of_pornography
Concept in epidemiology
Daniel Westreich and Sander Greenland in 2013. It is a concept in causal inference.[citation needed] In scientific papers reporting observational studies
Table_2_fallacy
Theory of response to surprise events
(April 2007). "Nassim Taleb's "The Black Swan"". Statistical Modeling, Causal Inference, and Social Science. Columbia University. Retrieved 23 May 2012. Gangahar
Black_swan_theory
Professor
econometrics and empirical microeconomics, and is a specialist in causal inference and program evaluation. He has made fundamental contributions to important
Alberto_Abadie
Chinese-American biostatistician
research topics have included survival analysis for complex data, causal inference in precision medicine, and the study of chronic diseases including
Ying_Ding_(biostatistician)
Canadian theoretical quantum physicist
Foils. Spekkens is a faculty member and the leader of the quantum causal inference initiative at Perimeter Institute for Theoretical Physics. He regularly
Robert_Spekkens
Historical model of economic growth
equilibrium Empirical methods Experimental Econometrics Time series Spatial Causal inference Quasi-experiments Prescriptive and policy Welfare analysis Social choice
Rostow's_stages_of_growth
Conditionals that discuss what would have been if things were otherwise
basis for causal inference in the natural and social sciences, since each structural equation in those domains corresponds to a familiar causal mechanism
Counterfactual_conditional
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