Explaining Model Behavior with Global Causal Analysis

,

[1]  Kathleen C. Fraser,et al.  Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models , 2022, TRUSTNLP.

[2]  David M. Markowitz,et al.  PassivePy: A Tool to Automatically Identify Passive Voice in Big Text Data , 2022, Journal of Consumer Psychology.

[3]  Luca Longo,et al.  A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods , 2021, Frontiers in Artificial Intelligence.

[4]  A. Chandar,et al.  Post-hoc Interpretability for Neural NLP: A Survey , 2021, ACM Computing Surveys.

[5]  Shafiq R. Joty,et al.  Reliability Testing for Natural Language Processing Systems , 2021, ACL.

[6]  Sainyam Galhotra,et al.  Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals , 2021, SIGMOD Conference.

[7]  C. Ouyang,et al.  Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications , 2021, Inf. Fusion.

[8]  N. C. Camgoz,et al.  D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery , 2021, ACM Comput. Surv..

[9]  Riccardo Guidotti,et al.  Evaluating local explanation methods on ground truth , 2021, Artif. Intell..

[10]  Mohit Bansal,et al.  Robustness Gym: Unifying the NLP Evaluation Landscape , 2021, NAACL.

[11]  Trevor Hastie,et al.  Causal Interpretations of Black-Box Models , 2019, Journal of business & economic statistics : a publication of the American Statistical Association.

[12]  Marco F. Huber,et al.  A Survey on the Explainability of Supervised Machine Learning , 2020, J. Artif. Intell. Res..

[13]  Himabindu Lakkaraju,et al.  Robust and Stable Black Box Explanations , 2020, ICML.

[14]  I. Shpitser,et al.  Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning , 2020, ArXiv.

[15]  Sameer Singh,et al.  Beyond Accuracy: Behavioral Testing of NLP Models with CheckList , 2020, ACL.

[16]  Alan S. Cowen,et al.  GoEmotions: A Dataset of Fine-Grained Emotions , 2020, ACL.

[17]  Yoav Goldberg,et al.  Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? , 2020, ACL.

[18]  Thomas Wolf,et al.  DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter , 2019, ArXiv.

[19]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[20]  Jaime S. Cardoso,et al.  Machine Learning Interpretability: A Survey on Methods and Metrics , 2019, Electronics.

[21]  P. Spirtes,et al.  Review of Causal Discovery Methods Based on Graphical Models , 2019, Front. Genet..

[22]  Vineeth N. Balasubramanian,et al.  Neural Network Attributions: A Causal Perspective , 2019, ICML.

[23]  Yonatan Belinkov,et al.  Analysis Methods in Neural Language Processing: A Survey , 2018, TACL.

[24]  Lin Li,et al.  How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis , 2018, Neural Computing and Applications.

[25]  Tim Miller,et al.  Contrastive explanation: a structural-model approach , 2018, The Knowledge Engineering Review.

[26]  Vineet K Raghu,et al.  Evaluation of Causal Structure Learning Methods on Mixed Data Types , 2018, CD@KDD.

[27]  Saif Mohammad,et al.  Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words , 2018, ACL.

[28]  Mark A. Neerincx,et al.  Contrastive Explanations with Local Foil Trees , 2018, ICML 2018.

[29]  Mikko Koivisto,et al.  Intersection-Validation: A Method for Evaluating Structure Learning without Ground Truth , 2018, AISTATS.

[30]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[31]  Aaron J. Fisher,et al.  All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously , 2018, J. Mach. Learn. Res..

[32]  Rich Caruana,et al.  Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation , 2017, AIES.

[33]  Jure Leskovec,et al.  Interpretable & Explorable Approximations of Black Box Models , 2017, ArXiv.

[34]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[35]  Osbert Bastani,et al.  Interpreting Blackbox Models via Model Extraction , 2017, ArXiv.

[36]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[37]  Clark Glymour,et al.  A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images , 2016, International Journal of Data Science and Analytics.

[38]  D. Apley,et al.  Visualizing the effects of predictor variables in black box supervised learning models , 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[39]  Carlos Guestrin,et al.  Model-Agnostic Interpretability of Machine Learning , 2016, ArXiv.

[40]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[41]  Joseph Y. Halpern.  A Modification of the Halpern-Pearl Definition of Causality , 2015, IJCAI.

[42]  Emil Pitkin,et al.  Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation , 2013, 1309.6392.

[43]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[44]  Thomas S. Richardson,et al.  Learning high-dimensional directed acyclic graphs with latent and selection variables , 2011, 1104.5617.

[45]  Jiji Zhang,et al.  On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias , 2008, Artif. Intell..

[46]  Jiji Zhang,et al.  Causal Reasoning with Ancestral Graphs , 2008, J. Mach. Learn. Res..

[47]  Kevin P. Murphy,et al.  Exact Bayesian structure learning from uncertain interventions , 2007, AISTATS.

[48]  Aapo Hyvärinen,et al.  A Linear Non-Gaussian Acyclic Model for Causal Discovery , 2006, J. Mach. Learn. Res..

[49]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[50]  Giles Hooker,et al.  Discovering additive structure in black box functions , 2004, KDD.

[51]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[52]  David Maxwell Chickering,et al.  Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..

[53]  P. Spirtes,et al.  Ancestral graph Markov models , 2002 .

[54]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[55]  Jin Tian,et al.  Causal Discovery from Changes , 2001, UAI.

[56]  Joseph Y. Halpern,et al.  Causes and Explanations: A Structural-Model Approach. Part I: Causes , 2000, The British Journal for the Philosophy of Science.

[57]  D. Allen.  Making things happen. , 2000, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[58]  Gregory F. Cooper,et al.  Causal Discovery from a Mixture of Experimental and Observational Data , 1999, UAI.

[59]  Jude W. Shavlik,et al.  Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.

[60]  J. Cussens,et al.  Kernel-based Approach for Learning Causal Graphs from Mixed Data , 2020, PGM.

[61]  R. Plutchik.  A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION , 1980 .