Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi The Case For Time in Causal DAGs | Philosophy of Science | Cambridge Core 2026-06-24T12:55:55+00:00 https://www.cambridge.org/core/journals/philosophy-of-science/article/case-for-time-in-causal-dags/FB8A5FA21249300B5250358B21A3E26D?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles cshalizi to:NB graphical_models causality philosophy_of_science https://pinboard.in/ https://pinboard.in/u:cshalizi/b:90a1d2ee3253/ Causal Direction in Causal Bayes Nets | Philosophy of Science | Cambridge Core 2025-08-16T13:16:48+00:00 https://www.cambridge.org/core/journals/philosophy-of-science/article/causal-direction-in-causal-bayes-nets/7CEA494FFE2E054D6EBB26C442B218DB?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles cshalizi to:NB causal_inference graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:36b69b86c1af/ [2308.07037] Bayesian Flow Networks 2025-03-10T13:55:12+00:00 https://arxiv.org/abs/2308.07037 cshalizi to:NB graphical_models neural_networks simulation statistics computational_statistics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4f89537c0697/ David Kinney & Tania Lombrozo, Building Compressed Causal Models of the World - PhilPapers 2024-12-11T19:55:08+00:00 https://philpapers.org/rec/KINBCC cshalizi to:NB cognitive_science graphical_models causality information_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ebc6286ac206/ Belief Network Analysis: A Relational Approach to Understanding the Structure of Attitudes1 | American Journal of Sociology: Vol 122, No 5 2024-12-11T19:50:45+00:00 https://www.journals.uchicago.edu/doi/abs/10.1086/691274 cshalizi to:NB causal_discovery ideology political_science sociology graphical_models of_course_its_really_a_spin_glass https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4281f10e72a8/ Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data 2024-12-09T21:40:14+00:00 https://proceedings.mlr.press/v151/baak22a.html cshalizi to:NB computational_statistics simulation graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a839fb404ad2/ [2206.08120] Simultaneous Estimation of Graphical Models by Neighborhood Selection 2023-06-28T16:15:37+00:00 https://arxiv.org/abs/2206.08120 cshalizi graphical_models model_selection in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7f61e925f50c/ Mapping ideologies as networks of ideas: Journal of Political Ideologies: Vol 0, No 0 2023-06-08T22:05:24+00:00 https://www.tandfonline.com/doi/abs/10.1080/13569317.2022.2138293?journalCode=cjpi20 cshalizi to:NB graphical_models ideology cognitive_science to_read re:democratic_cognition https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d643359afe28/ [2305.11561] Formalising causal inference in time and frequency on process graphs with latent components 2023-05-27T16:19:33+00:00 https://arxiv.org/abs/2305.11561 cshalizi time_series causality graphical_models fourier_analysis in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:41a08583d9d8/ Markov Properties and Quantum Experiments | SpringerLink 2023-05-01T19:56:32+00:00 https://link.springer.com/chapter/10.1007/1-4020-4876-9_5 cshalizi in_NB graphical_models causality quantum_mechanics have_read kith_and_kin glymour.clark cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5056035702c1/ [2206.05829] A non-graphical representation of conditional independence via the neighbourhood lattice 2022-06-15T17:48:21+00:00 https://arxiv.org/abs/2206.05829 cshalizi to:NB graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6293cf9ae283/ [2206.06124] Causal Discovery in Hawkes Processes by Minimum Description Length 2022-06-15T17:47:19+00:00 https://arxiv.org/abs/2206.06124 cshalizi to:NB point_processes graphical_models model_selection minimum_description_length https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dfa84229e268/ [2206.00416] In the Eye of the Beholder: Robust Prediction with Causal User Modeling 2022-06-09T10:12:23+00:00 https://arxiv.org/abs/2206.00416 cshalizi to:NB recommender_systems causal_inference graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a3ca652cf3ca/ [2206.01249] Single-World Intervention Graphs for Defining, Identifying, and Communicating Estimands in Clinical Trials 2022-06-09T08:32:05+00:00 https://arxiv.org/abs/2206.01249 cshalizi to:NB graphical_models causal_inference experimental_design https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1b9f0627bcae/ [2108.03099] Causal Inference Theory with Information Dependency Models 2022-06-06T12:55:51+00:00 https://arxiv.org/abs/2108.03099 cshalizi to:NB causality graphical_models information_theory color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ffb707aff322/ [2206.01152] Causal Structure Learning: a Combinatorial Perspective 2022-06-06T12:52:56+00:00 https://arxiv.org/abs/2206.01152 cshalizi to:NB causal_discovery graphical_models computational_statistics uhler.caroline to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dc366a20c919/ Total positivity in exponential families with application to binary variables 2021-08-10T14:09:30+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-3/Total-positivity-in-exponential-families-with-application-to-binary-variables/10.1214/20-AOS2007.short cshalizi to_read exponential_families graphical_models uhler.caroline lauritzen.steffen of_course_its_really_a_spin_glass in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c1464c953dee/ PsyArXiv Preprints | The Confidence Interval that Wasn’t: Bootstrapped “Confidence Intervals” in L1-Regularized Partial Correlation Networks 2021-07-19T13:52:15+00:00 https://psyarxiv.com/kjh2f cshalizi to:NB sparsity model_selection graphical_models statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f65b9477b29f/ [2105.02180] A unifying tutorial on Approximate Message Passing 2021-06-28T03:49:13+00:00 https://arxiv.org/abs/2105.02180 cshalizi to:NB graphical_models computational_statistics statistics samworth.richard_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:268aa73d4c33/ [2102.10324] Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables 2021-06-28T03:42:54+00:00 https://arxiv.org/abs/2102.10324 cshalizi to:NB causal_inference graphical_models statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:154daf02af07/ [2106.05694] Confidence in Causal Discovery with Linear Causal Models 2021-06-24T20:41:33+00:00 https://arxiv.org/abs/2106.05694 cshalizi to_read graphical_models causal_discovery confidence_sets drton.mathias in_NB statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2815f8e350f1/ [2106.03969] Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models 2021-06-10T02:24:36+00:00 https://arxiv.org/abs/2106.03969 cshalizi to:NB graphical_models chow-liu statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:529de0ce908f/ [2102.13384] Why did the distribution change? 2021-05-26T04:12:55+00:00 https://arxiv.org/abs/2102.13384 cshalizi to:NB explanation graphical_models causal_inference inequality to_teach:statistics_of_inequality_and_discrimination statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ad1b7d268f83/ [2105.10350] Definite Non-Ancestral Relations and Structure Learning 2021-05-24T22:52:26+00:00 https://arxiv.org/abs/2105.10350 cshalizi to:NB causal_discovery graphical_models shojaie.alie drton.mathias statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bae75ae66b9f/ [1611.06221] Foundations of Structural Causal Models with Cycles and Latent Variables 2021-05-12T18:09:18+00:00 https://arxiv.org/abs/1611.06221 cshalizi to:NB graphical_models causality peters.jonas https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f3134825315f/ Graphical Models for Processing Missing Data: Journal of the American Statistical Association: Vol 0, No 0 2021-04-12T03:43:38+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1874961 cshalizi to:NB missing_data pearl.judea graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:67193b91bb61/ [2101.09271] Representation of Context-Specific Causal Models with Observational and Interventional Data 2021-04-12T03:16:24+00:00 https://arxiv.org/abs/2101.09271 cshalizi to:NB graphical_models causal_inference https://pinboard.in/ https://pinboard.in/u:cshalizi/b:21234b5d4504/ [2006.11970] A polynomial-time algorithm for learning nonparametric causal graphs 2021-03-17T17:38:27+00:00 https://arxiv.org/abs/2006.11970 cshalizi to_read causal_discovery statistics graphical_models in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3bd512ec799e/ [2101.03501] Entropic Causal Inference: Identifiability and Finite Sample Results 2021-01-12T20:55:32+00:00 https://arxiv.org/abs/2101.03501 cshalizi to:NB causal_inference causal_discovery graphical_models information_theory color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8969f6a9698e/ [2101.03093] Learning non-Gaussian graphical models via Hessian scores and triangular transport 2021-01-11T16:32:41+00:00 https://arxiv.org/abs/2101.03093 cshalizi to:NB graphical_models sparsity statistics information_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fb9eca8344a8/ [2101.02332] Identification of Latent Variables From Graphical Model Residuals 2021-01-08T03:39:39+00:00 https://arxiv.org/abs/2101.02332 cshalizi to:NB causal_discovery graphical_models inference_to_latent_objects statistics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:653363dd410a/ Behavioral Implications of Causal Misperceptions | Annual Review of Economics 2021-01-03T19:35:30+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-072219-111921 cshalizi to:NB economics decision-making graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3a715566107e/ [2006.11937] Learning of Discrete Graphical Models with Neural Networks 2020-12-26T17:46:11+00:00 https://arxiv.org/abs/2006.11937 cshalizi to:NB graphical_models causal_discovery nonparametrics statistics neural_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:db060342bc93/ [2012.10980] Measurement bias: a structural perspective 2020-12-24T15:47:19+00:00 https://arxiv.org/abs/2012.10980 cshalizi to:NB causal_inference graphical_models measurement https://pinboard.in/ https://pinboard.in/u:cshalizi/b:353018eb50eb/ [2012.11281] Towards Conditional Path Analysis 2020-12-22T03:53:51+00:00 https://arxiv.org/abs/2012.11281 cshalizi to:NB graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6db972682890/ On Efficient Adjustment in Causal Graphs 2020-12-21T04:36:35+00:00 https://jmlr.org/papers/v21/20-175.html cshalizi to:NB graphical_models causal_inference maathuis.marloes didelez.vanessa https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f6cb91d47edd/ The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms - Rafael Quintana, 2020 2020-12-16T21:29:07+00:00 https://journals.sagepub.com/doi/abs/10.1177/0049124120926208 cshalizi to:NB to_read causal_discovery graphical_models education academia https://pinboard.in/ https://pinboard.in/u:cshalizi/b:214b1f21f62c/ [2012.08154] Inference of Causal Effects when Adjustment Sets are Unknown 2020-12-16T17:45:40+00:00 https://arxiv.org/abs/2012.08154 cshalizi to:NB causal_inference statistics graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dbc42aa2fb0e/ [2012.05269] Hard and Soft EM in Bayesian Network Learning from Incomplete Data 2020-12-12T18:13:27+00:00 https://arxiv.org/abs/2012.05269 cshalizi to:NB missing_data causal_discovery graphical_models em_algorithm statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4910988c3fe1/ Evans : Model selection and local geometry 2020-12-11T17:33:36+00:00 https://projecteuclid.org/euclid.aos/1607677245 cshalizi model_selection information_geometry statistics causal_discovery graphical_models to_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d9e2275be851/ A Model of Competing Narratives - American Economic Association 2020-11-30T16:04:28+00:00 https://www.aeaweb.org/articles?id=10.1257/aer.20191099 cshalizi to:NB to_read graphical_models model_selection psychology_by_economists color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d71c6317abb6/ [1810.10854] Structure learning of undirected graphical models for count data 2020-11-25T14:54:04+00:00 https://arxiv.org/abs/1810.10854 cshalizi to:NB graphical_models biochemical_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cb5740b4e4f1/ Algebraic Statistics in Practice: Applications to Networks | Annual Review of Statistics and Its Application 2020-11-19T20:04:23+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031017-100053 cshalizi to:NB statistics phylogenetics network_data_analysis graphical_models algebra petrovic.sonja uhler.caroline https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a80cef74bdd5/ Peng , Shen , Pan : Reconstruction of a directed acyclic graph with intervention 2020-11-18T21:01:14+00:00 https://projecteuclid.org/euclid.ejs/1605582080 cshalizi to:NB causal_discovery graphical_models experiments statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:278069bb5857/ Semidefinite Tests for Latent Causal Structures - IEEE Journals & Magazine 2020-11-16T16:04:42+00:00 https://ieeexplore.ieee.org/document/8804234 cshalizi to:NB causal_discovery graphical_models statistics hypothesis_testing inference_to_latent_objects https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9caefd16483a/ [2009.12246] Message passing for probabilistic models on networks with loops 2020-10-23T19:29:52+00:00 https://arxiv.org/abs/2009.12246 cshalizi to:NB graphical_models newman.mark to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3f47c41c9119/ [1903.03662] A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects 2020-07-28T15:15:20+00:00 https://arxiv.org/abs/1903.03662 cshalizi to:NB causality causal_inference graphical_models malinsky.daniel richardson.thomas shpitser.ilya https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cd3dd9da34d7/ Asymptotic Model Selection for Directed Networks with Hidden Variables | SpringerLink 2020-05-16T18:08:16+00:00 https://link.springer.com/chapter/10.1007/978-94-011-5014-9_16 cshalizi to:NB have_read to_reread information_criteria information_geometry statistics likelihood graphical_models inference_to_latent_objects re:HEAS https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2a65e9c78eb7/ An Axiomatic Characterization of Causal Counterfactuals | SpringerLink 2020-05-16T17:44:25+00:00 https://link.springer.com/article/10.1023/A:1009602825894 cshalizi to:NB causality graphical_models have_read pearl.judea galles.david https://pinboard.in/ https://pinboard.in/u:cshalizi/b:63ec3efb9a57/ [2004.10984] A Complete Characterization of Projectivity for Statistical Relational Models 2020-04-26T17:24:03+00:00 https://arxiv.org/abs/2004.10984 cshalizi to:NB to_read relational_learning graphical_models re:your_favorite_ergm_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8fbc31e54fec/ A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: fixing the chemical factory | SpringerLink 2020-04-17T15:54:19+00:00 https://link.springer.com/article/10.1007/s11229-018-02014-7 cshalizi to:NB causality graphical_models philosophy_of_science https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0ff441c15352/ Generalized Network Psychometrics: Combining Network and Latent Variable Models | SpringerLink 2020-01-26T16:32:39+00:00 https://link.springer.com/article/10.1007/s11336-017-9557-x cshalizi to:NB factor_analysis graphical_models borsboom.denny psychometrics inference_to_latent_objects statistics re:g_paper re:major_depression_qu'est-ce_que_c'est have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e10094c749c8/ [1801.08364] Model selection and local geometry 2020-01-12T22:32:28+00:00 https://arxiv.org/abs/1801.08364 cshalizi model_selection statistics graphical_models causal_discovery in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9050bf866786/ [1911.10114] Interventional Markov Equivalence for Mixed Graph Models 2020-01-12T22:21:53+00:00 https://arxiv.org/abs/1911.10114 cshalizi to:NB graphical_models causal_inference statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1a1753eead81/ On causal discovery with an equal-variance assumption | Biometrika | Oxford Academic 2019-12-03T19:43:35+00:00 https://academic.oup.com/biomet/article-abstract/106/4/973/5573229 cshalizi causal_discovery drton.mathias graphical_models in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c9d0cec37d70/ [1911.02700] Uncertainty relations and fluctuation theorems for Bayes nets 2019-11-11T15:12:39+00:00 https://arxiv.org/abs/1911.02700 cshalizi graphical_models fluctuation-response stochastic_processes wolpert.david in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9000814a5906/ [1910.12993] Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs 2019-10-30T13:39:42+00:00 https://arxiv.org/abs/1910.12993 cshalizi to:NB graphical_models causal_discovery zhang.kun statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2dd402192493/ [1901.10134] Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variances 2019-10-22T13:47:08+00:00 https://arxiv.org/abs/1901.10134 cshalizi to:NB graphical_models causal_inference statistics identifiability https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5fe1f546dce1/ [1910.02997] Identifying causal effects in maximally oriented partially directed acyclic graphs 2019-10-11T22:22:58+00:00 https://arxiv.org/abs/1910.02997 cshalizi to:NB causal_inference graphical_models statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0281b0d61af2/ [1807.02877] Moderated Network Models 2019-10-02T15:49:23+00:00 https://arxiv.org/abs/1807.02877 cshalizi to:NB graphical_models statistics borsboom.denny https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1de588ac00e9/ [1909.13186] Causal screening for dynamical systems 2019-10-01T16:19:21+00:00 https://arxiv.org/abs/1909.13186 cshalizi to:NB time_series causal_discovery graphical_models point_processes statistics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:71e9331e2bf9/ [1909.13189] Learning Sparse Nonparametric DAGs 2019-10-01T16:16:54+00:00 https://arxiv.org/abs/1909.13189 cshalizi causal_discovery graphical_models optimization statistics ravikumar.pradeep xing.eric sparsity to_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e6bdf1913768/ [1909.09141] Causal Modeling for Fairness in Dynamical Systems 2019-09-26T18:36:19+00:00 https://arxiv.org/abs/1909.09141 cshalizi graphical_models algorithmic_fairness in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bb077be7c3c8/ [1909.07186] Graph learning: How humans infer and represent networks 2019-09-25T04:07:58+00:00 https://arxiv.org/abs/1909.07186 cshalizi to:NB graphical_models cognitive_science psychology psychology_by_physicists color_me_skeptical bassett.danielle_s. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8733bccd2647/ [1909.09596] Non-Parametric Structure Learning on Hidden Tree-Shaped Distributions 2019-09-25T03:27:14+00:00 https://arxiv.org/abs/1909.09596# cshalizi0, for both noiseless and noisy settings. Subsequently, we apply our results on two classes of hidden models, namely, the M-ary erasure channel and the generalized symmetric channel, illustrating the usefulness and importance of our framework. As a byproduct of our analysis, this paper resolves the open problem of tree structure learning in the presence of non-identically distributed observation noise, providing explicit conditions on the convergence of the Chow-Liu algorithm under this setting as well."]]> to:NB graphical_models chow-liu_trees statistics sarwate.anand https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d6cc6279d578/ [1812.06282] A Generalization of Hierarchical Exchangeability on Trees to Directed Acyclic Graphs 2019-09-23T14:17:50+00:00 https://arxiv.org/abs/1812.06282 cshalizi to:NB graphical_models probability exchangeability https://pinboard.in/ https://pinboard.in/u:cshalizi/b:485f7baffd8e/ [1909.03586] Curve Fitting from Probabilistic Emissions and Applications to Dynamic Item Response Theory 2019-09-15T14:25:29+00:00 https://arxiv.org/abs/1909.03586 cshalizi to:NB inference_to_latent_objects graphical_models statistics psychometrics non-stationarity https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5b52b9085c6c/ [0804.4451] Dependence Structure Estimation via Copula 2019-09-15T14:23:29+00:00 https://arxiv.org/abs/0804.4451 cshalizi to:NB causal_discovery copulas statistics graphical_models color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:16fb254fdc52/ [1909.05418] The Global Markov Property for a Mixture of DAGs 2019-09-13T13:18:47+00:00 https://arxiv.org/abs/1909.05418 cshalizi to:NB causal_discovery graphical_models statistics color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ddc3bb1cc66d/ [1709.01577] Auto-G-Computation of Causal Effects on a Network 2019-08-26T23:46:23+00:00 https://arxiv.org/abs/1709.01577 cshalizi to:NB graphical_models causal_inference network_data_analysis statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e0ce00880176/