Publications

F. Richard Guo, James McQueen and Thomas S. Richardson.  (2020).  Empirical Bayes for large-scale randomized experiments: a spectral approach.  arXiv:2002.02564 .  44 pp..  [arXiv]

F. Richard Guo and Thomas S. Richardson.  (2020).  Chernoff-type concentration of empirical probabilities in relative entropy.  arXiv:2003.08614.  23 pp..  [arXiv]

Jiaqi Yin, Thomas S. Richardson and Linbo Wang.  (2019).  Multiplicative Effect Modeling: The General Case.  [arXiv]

Daniel Malinsky, Ilya Shpitser and Thomas Richardson.  (2019).  A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.  22nd International Conference on Artificial Intelligence and Statistics (AISTATS).  [web, arXiv]

F. Richard Guo and Thomas S. Richardson.  (2019).  On Testing Marginal versus Conditional Independence.  [arXiv]

Thomas S. Richardson.  (2018).  TikZ/PGF shape library for constructing Single-World Intervention Graphs (SWIGs).  [pdf, code]

T. S. Richardson, J. M. Robins, L. Wang.  (2018).  Contribution to discussion of Data-Driven Confounder Selection via Markov and Bayesian Networks.  by J. Häggström.  Biometrics, 74: 403-406.

S. A. Swanson, M. A. Hernán, M. Miller, J. M. Robins and T. S. Richardson.  (2018).  Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes.  Journal of the American Statistical Association, Reviews.  [doi]

I. Shpitser, R.J. Evans and T.S. Richardson.  (2018).  Acyclic linear SEMs obey the nested Markov property.  Thirty-Fifth Conference on Uncertainty in Artificial Intelligence.  [web]

T. S. Richardson, J. M. Robins and L. Wang.  (2017).  On modeling and estimation for the relative risk and risk difference.  Journal of the American Statistical Association, Theory and Methods, 519, 1121-1130.  [R package, arXiv]

L. Wang, T.S. Richardson and X.-H. Zhou.  (2017).  Causal analysis in multi-arm trials with truncation by death.  Journal of the Royal Statistical Society: Series B, 79, 719–735.  [arXiv]

L. Wang, J. M. Robins and T. S. Richardson.  (2017).  On falsification of the binary instrumental variable model.  Biometrika, 104(1): 229-236.  [arXiv]

L. Wang and T.S. Richardson.  (2017).   On the concordant survivorship assumption.  Statistics in Medicine, 36(4), 717-720.  [paper]

W. W. Loh, J. M. Robins and T. S. Richardson.  (2017).  An apparent paradox explained.  Contribution to discussion of A paradox from randomization-based causal inference.  by P. Ding.  Statistical Science, 32(3), 356-361.

T. S. Richardson, R. J. Evans, J. M. Robins and I. Shpitser.  (2017).  Nested Markov properties for acyclic directed mixed graphs.  Preprint, 67 pp.  [arXiv]

P. Nandy, M. H. Maathuis and T. S. Richardson.  (2017).  Estimating the effect of joint interventions from observational data in sparse high-dimensional settings.  Annals of Statistics, 45: 647-674.  [pdf, arXiv]

L. Wang, X.-H. Zhou and T. S. Richardson.  (2017).   Identification and estimation of causal effects with outcomes truncated by death.  Biometrika, 104: 597-612.  [arXiv]

L. Wang, T. S. Richardson, J. M. Robins.  (2017).  Congenial Causal Inference with Binary Structural Nested Mean Models.  Preprint, 31 pp.  [arXiv]

T. S. Richardson and J. M. Robins.  (2016).  Contribution to discussion of read paper Causal inference using invariant prediction: identification and confidence intervals.  by J. Peters, P. Bühlmann, N. Meinshausen.  Journal of the Royal Statistical Society: Series B, Vol. 78, pp. 1003-1004.  [pdf]

J.Y. Huang, A.R. Gavin, T.S. Richardson, A. Rowhani-Rahbar, D.S. Siscovick, D.A. Enquobahrie.  (2015).  Are early-life socioeconomic conditions directly related to birth outcomes? Grandmaternal education, grandchild birth weight, and associated bias analyses (with discussion).  American Journal of Epidemiology, 182(7):568-78.

W. W. Loh and T. S. Richardson.  (2015).  A finite population likelihood ratio test of the sharp null hypothesis for compliers.  Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, Amsterdam, 06/12/2015.  (Tom Heskes and Marina Meila, Eds.).  [pdf]

R. J. Evans and T. S. Richardson.  (2015).  Smooth, identifiable supermodels of discrete DAG models with latent variables.  Preprint.  30 pp..  [arXiv]

R.J. Evans and T.S. Richardson.  (2014).  Markovian acyclic directed mixed graphs for discrete data.  Annals of Statistics, 42(4), pp.1452-1482.  [doi, arXiv]

I.Shpitser, R.J. Evans, T.S. Richardson, J.M. Robins.  (2014).  Introduction to nested Markov models.  Behaviormetrika, 41(1): pp. 3-39.

D. Heckerman, C. Meek, T.S. Richardson.  (2014).  Variations on undirected graphical models and their relationships.  Kybernetica, Vol. 50, No. 3.  [paper]

T. S. Richardson and J. M. Robins.  (2014).  ACE Bounds; SEMs with Equilibrium Conditions.  Contribution to discussion of Instrumental Variables: An Econometrician’s Perspective.  by G. Imbens.  Statistical Science, Vol. 29, pp.363-366.  [arXiv]

T. S. Richardson and A. Rotnitzky.  (2014).  Causal Etiology of the Research of James M. Robins.  Statistical Science, Vol. 29, pp. 459-484.  [arXiv]

R.J. Evans and T.S. Richardson.  (2013).  Marginal log-linear parameters for graphical Markov models.  Journal of the Royal Statistical Society, Ser. B , vol. 75, Issue 4, pp. 743 – 768.  [arXiv]

M. Banerjee and T.S. Richardson.  (2013).  Exchangeable Bernoulli Random Variables and Bayes' Postulate.  Electronic Journal of Statistics, vol. 7, pp. 2193 - 2208.  [ejs]

C. Chelba, P. Xu, F. Pereira, T. S. Richardson.  (2013).  Large Scale Distributed Acoustic Modeling with Back-off N-grams.  IEEE Transactions on Audio, Speech and Language Processing, vol. 21, pp. 1158 - 1169.  [arXiv]

T.S. Richardson and J.M. Robins.  (2013).  Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality.  Submitted to Foundations and Trends in Machine Learning.  [slides, video, Tech Report]

T.S. Richardson and J.M. Robins.  (2013).  Single World Intervention Graphs: A Primer.   Second UAI Workshop on Causal Structure Learning, Bellevue, Washington, 07/15/2013.  [pdf]

W.W. Loh and T.S. Richardson.  (2013).  A finite population test of the sharp null hypothesis for Compliers.  Second UAI Workshop on Causal Structure Learning, Bellevue, Washington, 07/15/2013.  [pdf]

I. Shpitser, R.J. Evans, T.S. Richardson, J.M. Robins.  (2013).  Sparse Nested Markov Models with Log-Linear Parameters.  Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence.  (A. Nicholson and P. Smyth, Eds.).  [arXiv]

T.J. vanderWeele, T.S. Richardson.  (2012).  General theory for interactions in sufficient cause models with dichotomous exposures.  Annals of Statistics, 40, pp. 2128 - 2161.  [arXiv]

D. Colombo, M.H. Maathuis, M. Kalisch and T.S. Richardson.  (2012).  Learning high-dimensional directed acyclic graphs with latent and selection variables.  Annals of Statistics, 40: pp. 294-321.  [arXiv]

C. Chelba, P. Xu, F. Pereira, T. S. Richardson.  (2012).  Distributed Acoustic Modeling with Back-off N-grams.  IEEE International Conference on Acoustics, Speech and Signal Processing.  [pdf]

T.S. Richardson and J.M. Robins.  (2012).  Contribution to discussion of Experimental Designs for Identifying Causal Mechanisms.  by K. Imai, D. Tingley and T. Yamamoto.  Journal of the Royal Statistical Society, Series A.

X. de Luna, T . S. Richardson and I. Waernbaum.  (2011).  Identification of covariates for the non - parametric estimation of an average treatment effect.  Biometrika, 98(4): pp. 861 - 875.  [doi]

T.S. Richardson, R.J. Evans and J.M. Robins.  (2011).  Transparent Parameterizations of Potential Outcome Models (with discussion).  In Bayesian Statistics 9, pp. 569-610.  [corrected, errata]

I. Shpitser, T.S. Richardson and J. M. Robins.  (2011).  An efficient algorithm for computing interventional distributions in latent variable causal models.  Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence.  (F. Cozman and A. Pfeffer, Eds.).  [arXiv]

J. Robins and T.S. Richardson.  (2011).  Alternative Graphical Causal Models and the Identification of Direct Effects.  Chapter 6.  Causality and Psychopathology: Finding the Determinants of Disorders and their Cures, pp. 1-52.  (P. Shrout, K. Keyes and K. Ornstein, Eds.) Oxford University Press.  [Tech Report]

S. Srinivasan, T. Gross, S. Bain, B. Ausk, J. Prasad and T.S. Richardson.  (2010).  Rescuing Loading Induced Bone Formation at Senescence.  PLoS Comp Bio, doi: 10.137/journal.pcbi.1000.924.

S. Chaudhuri , T.S. Richardson , J. Robins, and E. Zivot.  (2010).  Split - Sample Score Tests in Linear Instrumental Variables Regression.  Econometric Theory, 26: pp. 1820 - 1837.

A. Glynn, T.S. Richardson and M. Handcock.  (2010).  Resolving Contested Elections: The Limited Power of Post-Vote Vote Choice Data.  Journal of the American Statistical Association, 105(489): 84-91.

E.E. Moodie and T.S. Richardson.  (2010).  Bias Correction in Non-Differentiable Estimating Equations for Optimal Dynamic Regimes.  Scandinavian Journal of Statistics, 37, pp. 126-146.

R. J. Evans and T.S. Richardson.  (2010).  Maximum Likelihood Fitting of Acyclic Directed Mixed Graphs to Binary Data.  Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence.  (P. Spirtes and P. Grunwald, Eds.).

T.S. Richardson and J.M. Robins.  (2010).  Analysis of the Binary Instrumental Variable Model.  Chapter 25.  Heuristics, Probability and Causality: A Tribute to Judea Pearl.  pp. 83-105.  (R. Dechter, H. Geffner and J. Y. Halpern, Eds.) Oxford University Press.  [Tech Report]

M. Drton, M. Eichler, T.S. Richardson.  (2009).  Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors.  Journal of Machine Learning Research, 10, 1989 - 2008.

A. Ali, T.S. Richardson and P. Spirtes.  (2009).  Markov Equivalence for Ancestral Graphs.  Annals of Statistics, 37, pp. 2808-2837.  [jstor, euclid]

T.S. Richardson.  (2009).  A factorization criterion for acyclic directed mixed graphs.  Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence.  (J. Bilmes and A. Ng, Eds.).

I. Shpitser, T.S. Richardson and J. M. Robins.  (2009).  Testing Edges by Truncation.  Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence.

J.M. Robins, T.S. Richardson and P. Spirtes.  (2009).  On Identification and Inference for Direct Effects.  UW Dept. of Statistics Technical Report No. 563.

M. Drton, T.S. Richardson.  (2008).  Binary Models for Marginal Independence.  Journal of the Royal Statistical Society, Ser. B 70(2), pp. 287 - 309.  [jstor]

M. Drton and T.S. Richardson.  (2008).  Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models.  Journal of Machine Learning Research, 9 pp. 581-602.  [jmlr, arXiv]

A. Glynn, J. Wakefield, M. Handcock and T.S. Richardson.  (2008).  Alleviating Linear Ecological Bias and Optimal Design with Subsample Data.  Journal of the Royal Statistical Society, Ser. A 171(1) pp. 179-202.

S.L. Lauritzen and T.S. Richardson.  (2008).  Contribution to discussion of paper on Sampling Bias and Logistic Models.  by P. McCullagh, J. Roy.  Statist. Soc., B 70, p. 671.

S. Chaudhuri, M. Drton and T.S. Richardson.  (2007).  Estimation of a Covariance Matrix with Zeros.  Biometrika, 94(1), pp. 199-216.  [jstor]

E.E. Moodie, T.S. Richardson and D. Stephens.  (2007).  Demystifying Optimal Dynamic Treatment Regimes.  Biometrics, 63(2), pp. 447-455.

S. Srinivasan, B.J. Ausk, S.L. Poliachik, S.E. Warner, T.S. Richardson and T.S Gross.  (2007).  Rest-inserted Loading Rapidly Amplifies the Response of Bone to Small Increases in Strain and Load Cycles.  Journal of Applied Physiology, 102: 1945-1952.

T.S. Richardson, L. Schulz and A. Gopnik.  (2007).  Data-mining probabilists or experimental determinists? : A Dialogue on the Principles underlying Causal Learning in Children.  Causal Learning: Psychology, Philosophy and Computation, (A. Gopnik and L. Schulz, Eds.) Oxford: Oxford University Press.

J. Robins, T.J. vanderWeele and T.S. Richardson.  (2007).  Contribution to discussion of Causal Effects in the presence of non compliance a latent variable interpretation.  by A. Forcina.  Metron, LXIV (3) pp. 288-298.

J.A. Wegelin, A. Packer and T.S. Richardson.  (2006).  Latent models for cross-covariance.  Journal of Multivariate Analysis, 97(1): 79-102.

(2006).  Proceedings of the Twenty-Second Conference on Uncertainty and Artificial Intelligence.  (R. Dechter and T.S. Richardson, Eds.) AUAI Press.

M. Miyamura, T.S. Richardson.  (2006).  Bi-partial covariances and Gaussian ancestral graph models.  Manuscript.

E. Moodie and T.S. Richardson.  (2005).  A new variance for recursive g-estimation of optimal dynamic treatment regimes.  Proceedings, WNAR 2005.

A. Ali, T.S. Richardson, P. Spirtes and J. Zhang.  (2005).  Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables.  Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence.  p. 10-17.  (F. Bacchus and T. Jaakkola, Eds.).

M. Drton and T.S. Richardson.  (2004).  Multimodality of the likelihood in the bivariate seemingly unrelated regression model.  Biometrika, 91(2): pp. 383-392.

M. Drton and T.S. Richardson.  (2004).  Iterative Conditional Fitting for Gaussian Ancestral Graph Models.  Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence.  130-137.  (Outstanding student paper award).

A. Ali and T.S. Richardson.  (2004).  Searching across Markov equivalence classes of maximal ancestral graphs.  Proceedings, JSM 2004.

T.S. Richardson.  (2004).  Contribution to discussion of paper on Ecological Inference for 2x2 Tables.  by J. Wakefield.  Journal of the Royal Statistical Society, Ser. A 167(3), p.438.  [jstor]

T.S. Richardson.  (2003).  Markov Properties for Acyclic Directed Mixed Graphs.  The Scandinavian Journal of Statistics, March 2003, vol. 30, no. 1, 145-157.  [jstor]

M. Banerjee and T.S. Richardson.  (2003).  On dualization of graphical Gaussian models: a correction.  The Scandinavian Journal of Statistics, March 2003, vol. 30, 817-820.

M. Drton and T.S. Richardson.  (2003).  A new algorithm for maximum likelihood estimation in Gaussian graphical models for marginal independence.  Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence.  pp. 184-191.

S. Chaudhuri and T.S. Richardson.  (2003).  Using the structure of d-connecting paths as a qualitative measure of the strength of dependence.  Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence.  pp. 116-123.

T.S. Richardson and P. Spirtes.  (2003).  Causal Inference via ancestral graph Markov models (with discussion).  Highly Structured Stochastic Systems.  pp. 83-105.  (Peter Green, Nils Hjort and Sylvia Richardson, Eds.) Oxford University Press.

S. Lauritzen and T.S. Richardson.  (2002).  Chain Graph models and their causal interpretations (with discussion).  Journal of the Royal Statistical Society, Series B. 64(3), 321-363.

T.S. Richardson and P. Spirtes.  (2002).  Ancestral Graph Markov Models.  Annals of Statistics, 30, 962-1030.  [euclid, jstor, errata]

M. Townsend and T.S. Richardson.  (2002).  Probability and Statistics in the Legal Curriculum: A Case Study in Disciplinary Aspects of Interdisciplinarity.  Duquesne Law Review, 40(3), pp. 447-488.

G. Zweig, J. Bilmes, T. Richardson, K. Filali, K. Livescu, P. Xu, K. Jackson, Y. Brandman, E. Sandness, E. Holtz, J. Torres, B. Byrne.  (2002).  Structurally discriminative graphical models for automatic speech recognition - results from the 2001 Johns Hopkins summer workshop.  Proceedings, IEEE Conf. On Acoustics, Speech and Signal Processing.

A. Ali and T.S. Richardson.  (2002).  Markov equivalence classes for maximal ancestral graphs.  Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence.  pp. 1-9.

T.R. Hammond, G.L. Swartzman and T.S. Richardson.  (2001).  Bayesian estimation of fish school cluster composition applied to a Bering Sea acoustic survey.  ICES Journal of Marine Science, Vol. 58, No. 6, Nov 2001, pp. 1133-1149.

A. Ali, A. Murua and T.S. Richardson.  (2001).  A Comparison of Traditional Methods and Sequential Bayesian Methods for Blind Deconvolution Problems.  Proceedings, EUSIPCO 2002.  27 pp..

J.A. Wegelin and T.S. Richardson.  (2001).  Cross-covariance modeling via DAGs with hidden variables.  Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence.  pp. 546-553.

(2001).  Proceedings of the Eighth International Conference on Artificial Intelligence and Statistics.  (T. Jaakkola and T.S. Richardson, Eds.).

(2001).  Rank-One Latent Models for Cross-Covariance.  UW Department of Statistics, Technical Report, No. 391.  29 pp..

T.S. Richardson.  (2001).  Chain graphs which are maximal ancestral graphs are recursive causal graphs.  UW Department of Statistics, Technical Report, No. 387.  13 pp..

T.S. Richardson.  (2000).  Prediction and Model Selection.  Statistics and Computing.

J. Brutlag and T.S. Richardson.  (1999).  A Block Sampling Approach to Distinct Value Estimation.  Journal of Computational and Graphical Statistics, 11 (2), pp. 389-404.

T.S. Richardson, H. Bailer and M. Banerjee.  (1999).  Tractable Structure Search in the Presence of Latent Variables.  Proceedings of Artificial Intelligence and Statistics '99.  pp. 142-151.  (D. Heckerman and J. Whittaker, Eds.) Morgan Kaufmann, San Francisco, CA.

G. Ridgeway, D. Madigan and T.S. Richardson.  (1999).  Boosting Methodology for Regression Problems.  Proceedings of Artificial Intelligence and Statistics '99.  pp. 152-161.  (D. Heckerman and J. Whittaker, Eds.) Morgan Kaufmann, San Francisco, CA.

T.S. Richardson.  (1999).  A Local Markov Property for Acyclic Directed Mixed Graphs.  Proceedings, ISI Conference, Helsinki 1999, 4 pp..

T.S. Richardson, H. Bailer and M. Banerjee.  (1999).  Specification Searches Using MAG Models.  Proceedings, ISI Conference, Helsinki 1999, 4 pp..

S. Andersson, D. Madigan, M. Perlman and T.S. Richardson.  (1999).  Graphical Markov Models in Multivariate Analysis.  Multivariate Analysis, Design of Experiments, and Survey Sampling.  (S. Ghosh, Eds.) Marcel Dekker.

T.S. Richardson and P. Spirtes.  (1999).  Automated discovery of linear feedback models.  Computation. Causation and Discovery, pp. 253-302.  (C. Glymour and G. Cooper, Eds.) MIT Press.

R. Scheines, C. Glymour, P. Spirtes, C. Meek and T.S. Richardson.  (1999).  Truth is among the best explanations: Finding causal explanations of conditional independence and dependence.  pp. 167-209.  (C. Glymour and G. Cooper, Eds.) MIT Press.

P. Spirtes, C. Meek and T.S. Richardson.  (1999).  An algorithm for causal inference in the presence of latent variables and selection bias.  Computation. Causation and Discovery, pp. 211-252.  (C. Glymour and G. Cooper, Eds.) MIT Press.

G. Glymour, P. Spirtes and T.S. Richardson.  (1999).  On the possibility of inferring causation from association without background knowledge.  A response to a paper by J. Robins and L. Wasserman, and reply to a rejoinder. Computation. Causation and Discovery, pp. 323-332, pp. 343-345.  (C. Glymour and G. Cooper, Eds.) MIT Press.

T.S. Richardson.  (1999).  Discussion of Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models.  by B. Thiesson, M. Chickering, D. Heckerman and C. Meek..  Bayesian Statistics 6.

G. Ridgeway, T.S. Richardson and D. Madigan.  (1999).  Discussion of Bump Hunting in High-Dimensional Data.  by J. Friedman and N. Fisher.  Statistics and Computing, 9(2), pp. 150-152.

N. Wermuth, D.R. Cox, T.S. Richardson and G. Glonek.  (1999).  On transforming and generating cyclic graph models.  ZUMA Technical Report.  12 pp..

R. Scheines, C. Glymour, P. Spirtes, C. Meek and T.S. Richardson.  (1998).  The TETRAD Project: Constraint Based Aids to Model Specification (with discussion).  Multivariate Behavioral Research, 33(1) pp. 65-118.

P. Spirtes, T.S. Richardson, C. Meek, R. Scheines and C. Glymour.  (1998).  Using Path Diagrams as a Structural Equation Modelling Tool.  Sociological Methods and Research, 27 (2), pp. 182-225.

G. Ridgeway, D. Madigan, T.S. Richardson and J. O'Kane.  (1998).  Interpretable Boosted Naive Bayes Classification.  Proceedings of the Fourth International Conference on Knowledge, Discovery and Data Mining.  pp. 101-104.  (R. Agrawal, P. Stolorz, G. Piatetsky-Shapiro, Eds.).

T.S. Richardson.  (1998).  Chain Graphs and Symmetric Associations.  Learning in Graphical Models.  pp. 231-259.  (M. Jordan, Eds.) Kluwer.  (republished, 1999, MIT Press).

T.S. Richardson.  (1997).  A Characterization of Markov Equivalence for Directed Cyclic Graphs.  International Journal of Approximate Reasoning, 17, 2/3 (Aug.-Oct. 97), pp. 107-162.

G. Cooper, C. Aliferis, R. Ambrosino, J. Aronis, B. Buchanan, R. Caruana, M. Fine, C. Glymour, G. Gordon, B. Hanusa, J. Janosky, C. Meek, T. Mitchell, T.S. Richardson, P. Spirtes.  (1997).  An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality.  Artificial Intelligence and Medicine, 9, pp. 107-138.

T.S. Richardson.  (1997).  Extensions of Undirected and Acyclic, Directed Graphical Models.  Proceedings of Artificial Intelligence and Statistics '97.  pp. 421-428.  (D. Madigan and P. Smyth, Eds.).

T.S. Richardson, P. Spirtes and C. Glymour.  (1997).  A Note on Cyclic Graphs and Dynamical Feedback Systems.  Proceedings of Artificial Intelligence and Statistics '97.  pp. 421-428.  (D. Madigan and P. Smyth, Eds.).

P. Spirtes and T.S. Richardson.  (1997).  A Polynomial Time Algorithm for Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias.  Proceedings of Artificial Intelligence and Statistics '97.  pp. 489-500.  (D. Madigan and P. Smyth, Eds.).

P. Spirtes, C. Meek, T.S. Richardson.  (1997).  Heuristic Greedy Search Algorithms for Latent Variable Models.  Proceedings of Artificial Intelligence and Statistics '97.  pp. 481-488.  (D. Madigan and P. Smyth, Eds.).

T.S. Richardson.  (1997).  Review of An Introduction to Bayesian Networks.  Journal of the American Statistical Association.  (92 (439) pp. 1215-1216).  F.V. Jensen.

T.S. Richardson.  (1996).  A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models.  Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence.  pp.462-469.  Portland, Oregon, (E. Horvitz and F. Jensen, Eds.) Morgan Kaufmann, San Francisco, CA.

T.S. Richardson.  (1996).  A Discovery Algorithm for Directed Cyclic Graphs.  Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence.  pp. 428-487.  Portland, Oregon, (E. Horvitz and F. Jensen, Eds.) Morgan Kaufmann, San Francisco, CA.

P. Spirtes, T.S. Richardson, C. Meek, R. Scheines and C. Glymour.  (1996).  Using d-separation to calculate zero partial correlations in linear models with correlated errors.  Technical Report, CMU-PHIL-72.  17 pp..

P. Spirtes, C. Meek and T.S. Richardson.  (1995).  Causal Inference in the Presence of Latent Variables and Selection Bias.  Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence.  pp. 482-487.  Morgan Kaufmann, San Francisco, CA.

T.S. Richardson.  (1994).  Equivalence in Non-Recursive Structural Equation Models.  Proceedings of The 11th Symposium on Computational Statistics.  pp. 482-487.  COMPSTAT, Vienna, Austria, 08/20/1994.  (R. Dutter, Eds.) Physica Verlag, Vienna.