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The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice.
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Serum uric acid and cardiovascular mortality: the NHANES I epidemiologic follow-up study
J. Fang and M.H. Alderman · 2000
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Learning support vectors for face verification and recognition
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Instrumental variables and the search for identification: From supply and demand to natural experiments
J.D. Angrist and A.B. Krueger · 2001
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Pattern Classification
R. O. Duda, P. E. Hart, and D. G. Stork · 2001
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The mind’s arrows: Bayes nets and graphical causal models in psychology
C. Glymour · 2001
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Theory-based causal inference
J. B. Tenenbaum and T.L. Griffiths · 2002
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Mindless statistics
G. Gigerenzer · 2004
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Estimating mutual information
A. Kraskov, H. Stogbauer, and P. Grassberger · 2004
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Principles and practice of structural equation modeling
R.B. Kline · 2005
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Causal inference using potential outcomes: Design, modeling, decisions
D. B. Rubin · 2005
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The correlation coefficient: An overview
A.G. Asuero, A. Sayago, and A.G. Gonzalez · 2006
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Pattern Recognition and Machine Learning
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Elements of information theory
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Data analytic techniques for dynamical sytems
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Data analysis using regression and multilevel/hierarchical models
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Beware of the DAG!
A.P. Dawid · 2008
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Causal analysis in population studies , chapter Causation as a generative process. The elaboration of an idea for the social sciences and an application to an analysis of an interdependent dynamic social system
H.P. Blossfeld · 2009
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Discovering statistics using SPSS
A. Field · 2009
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Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman · 2009
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Circular analysis in systems neuroscience: the danges of double dipping
N. Kriegeskorte, W.K. Simmons, P.S. Bellgowan, and C.I. Baker · 2009
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Causality
J. Pearl · 2009
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GANITE: Estimation of individualized treatment effects using generative adversarial nets
J. Yoon, J. Jordan, and M. van der Schaar · 2009
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Separated at birth: statistcians, social scientists, and causality in health services research
B. E. Dowd · 2010
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Probabilistic latent variable models for distinguishing between cause and effect
J. M. Mooij, O. Stegle, D. Janzing, K. Zhang, and B. Scholkopf · 2010
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Are face-detection cameras racist?
A. Rose · 2010
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To explain or to predict?
G. Shmueli · 2010
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Critical comments on dynamic causal modelling
G. Lohmann, K. Erfurth, K. Muller, and R. Turner · 2011
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Targeted Learning - Causal Inference for Observational and Experimental Data
M. J. van der Laan and S. Rose · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Machine Learning: A probabilistic Perspective
K. P Murphy · 2012
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Information transfer in social media
G. V. Steeg and A. Galstyan · 2012
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Invited commentary: Structural equation models and epidemiologic analysis
T. J. VanderWeele · 2012
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Counterfactual reasoning and learning systems: the example of computational advertising
L. Bottou, J. Peters, Quinonero-Candela J., D. X. Charles, D. M. Chickering, E. Portugaly, D. Ray, P. Simard, and E. Snelson · 2013
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The garden of forking paths: why multiple comparisons can be a problem even when there is no ‘fishing expedition’ or ‘p-hacking’ and the research hypothesis was posited ahead of time, 2013
A. Gelman and E. Loken · 2013
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On the interpretation of weight vectors of linear models in multivariate neuroimaging
S. Haufe, F. Meinecke, K. Gorgen, S. Dahne, J-D. Haynes, B. Blankertz, and F. Biebmann · 2013
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Some surprising facts about (the problem of) surprising facts
D. Mayo · 2013
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Information-theoretic measures of influence based on content dynamics
G.V. Steeg and A. Galstyan · 2013
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Correlation does not even imply correlation
A. Gelman · 2014
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Equitability, mutual information, and the maximal information coefficient
J.B. Kinney and G.S. Atwal · 2014
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Sailing from the seas of chaos into the corridor of stability: practical recommendations to increase the informational value of studies
D. Lakens and E.R.K. Evers · 2014
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Entering the era of data science: targeted learning and the integration of statistics and computational data analysis
M. J. van der Laan and R. J. C. M. Starmans · 2014
Targeted Learning in Data Science
M. J. van der Laan and S. Rose · 2018
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Counterfactual explanations without opening the black box: automated decisions and the GDPR
S. Wachter, B. Mittelstadt, and C. Russell · 2018
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K. Aas, M. Jullum, and A. Loland · 2019
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Demystifying black-box models with symbolic metamodels
A.M. Alaa and M. van der Schaar · 2019
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Reconciling modern machine-learning practice and the classical bias-variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
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Causal processes in psychology are heterogeneous
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Estimating the reproducibility of psychological science
A. A. Aarts et al · 2015
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Efficient estimation of mutual information for strongly dependent variables
S. Gao, G.V. Steeg, and A. Galstyan · 2015
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Causal inference for statistics, social, and biomedical sciences. An Introduction
G.W. Imbens and D.B. Rubin · 2015
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A short history of the weight-importance effect and a recommendation for pre-testing: commentary on ebersole et al. (2016)
N.B. Jostmann, D. Lakens, and T.W. Schubert · 2015
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A cautionary note on testing latent variable models
I. Ropovik · 2015
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A short (personal) future history of revolution 2.0
B.A. Spellman · 2015
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N. Bolger, K.S. Zee, M. Rossignac-Milon, and Hassin R.R · 2019
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A meta-analytical answer to the crisis of confidence of psychology
J. Botella and J.I. Duran · 2019
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Toward gender-inclusive coreference resolution
Y. T. Cao and H. Daume III · 2019
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Failing grade: 89 percent of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly
S.A. Cassidy, R. Dimova, B. Giguere, J.R. Spence, and D.J. Stanley · 2019
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Preregistration: Comparing dream to reality
A. Claesen, S.L.B.T. Gomes, F. Tuerkinckx, and W. Vanpaemel · 2019
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Avoid Cohen’s ‘small’, ‘medium’, and ‘large’ for power analysis
J. Correll, C. Mellinger, G.H. McCelland, and C.M. Judd · 2019
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On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility and alternatives
A. D‘Amour · 2019
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Three ways to make replication mainstream
M.A. Gernsbacher · 2019
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Review of causal discovery methods based on graphical models
C. Glymour, K. Zhang, and P. Spirtes · 2019
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Cause effect pairs in machine learning , chapter Learning bivariate functional cusal model
O. Goudet, D. Kalainathan, M. Sebag, and I. Guyon · 2019
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Machine learning in policy evaluation: new tools for causal inference
N. Kreif and K. DiazOrdaz · 2019
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Does gender matter? towards fairness in dialogue systems
H. Liu, J. Dacon, W. Fan, H. Liu, and J. Liu, Z.and Tang · 2019
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On the fairness of disentangled representations
F. Locatello, G. Abbati, T. Rainforth, T. Bauer, S. Bauer, B. Scholkopf, and O. Bachem · 2019
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The M4 competition: 100,000 time series and 61 forecasting methods
S. Makridakis, E. Spiliotis, and V. Assimakopoulos · 2019
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Addressing the theory crisis in psychology
K. Oberauer and S. Lewandowsky · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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Research in social psychology changed between 2011 and 2016: larger sample sizes, more self-report measures, and more online studies
K. Sassenberg and L. Ditrich · 2019
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Causality for machine learning
B. Scholkopf · 2019
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Adapting neural networks for the estimation of treatment effects
C. Shi, D. M. Blei, and V. Veitch · 2019
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Fooled by correlation: Common misinterpretations in social science
N. Taleb · 2019
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The blessings of multiple causes
Y. Wang and D. M. Blei · 2019
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The generalizability crisis
T. Yarkoni · 2019
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2020
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Power contours: optimising sample size and precision in experimental psychology and human neuroscience
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True to the model or true to the data?
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A crash course in good and bad controls
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Targeted learning: Robust statistics for reproducible research
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The taboo against explicit causal inference in nonexperimental psychology
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Differentiable causal backdoor discovery
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Is peer review a good idea?
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Machine learning uncovers the most robust self-report predictors of relationships quality across 43 longitudinal couples studies
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