david blei causality

(C Proof of lemma:strongignorabilityfunctional) 19 0 obj << /D (appendix.D) /S /GoTo >> 92 0 obj How can we answer causal questions with machine learning, statistics, and data science? 52 0 obj 128 0 obj endobj There was also a series of enlightening lectures by Stanford professor Trevor Hastie, whose statistical learning books have become every Statistics students’ Bible! endobj endobj endobj 16 0 obj This book offers a self-contained and concise introduction to causal models and how to learn them from data. 67 0 obj << /D (appendix.J) /S /GoTo >> endobj 23 0 obj (J Proof of thm:conditionalpoidentify) endobj His research is conducted in collaboration with David Blei, his adviser. endobj Topics include: causality as a hypothetical intervention; the causal hierarchy of observe, act, imagine; causal graphical models (and how they are different from Bayesian networks); backdoor adjustment and the backdoor criteria; structural causal models … 159 0 obj << /D (subsection.3.2) /S /GoTo >> << /D (subsection.2.4) /S /GoTo >> 40 0 obj David Joseph Bohm (né le 20 décembre 1917, mort le 27 octobre 1992) est un physicien américain qui a réalisé d'importantes contributions en physique quantique, physique théorique, philosophie et neuropsychologie.Il a participé au projet Manhattan et conduit des entretiens filmés avec le philosophe indien Krishnamurti. 83 0 obj Mar 4, 2013 - "Causality" is a new piece in which microscopic biological imagery is used to blur the lines between figurative representation and abstraction. Or voilà un compliment, je crois, dont David Hume se serait bien passé. 152 0 obj endobj << /D (subsection.3.1) /S /GoTo >> (2.3 The identification strategy of the deconfounder) << /D (subsection.4.2) /S /GoTo >> 87 0 obj 131 0 obj David M. Bleia,b,c,1 and Padhraic Smythd,e Edited by Peter J. Bickel, University of California, Berkeley, CA, and approved June 16, 2017 (received for review March 15, 2017) Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. Topics include: causality as a hypothetical intervention; the causal hierarchy of observe, act, imagine; causal graphical models (and how they are different from Bayesian networks); backdoor adjustment and the backdoor criteria; structural causal models and counterfactuals; estimating counterfactuals with abduction; the potential outcomes framework (and its relationship to structural causal models). endobj Achetez et téléchargez ebook Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (English Edition): Boutique Kindle - Probability & Statistics : Amazon.fr (H Proof of thm:deconfounderfactor) 107 0 obj tensorflow pytorch: Text as outcome. I am a postdoctoral research scientist at the Columbia University Data Science Institute, working with David Blei. 135 0 obj 64 0 obj Others use the terms like counterfactual machine learning or counterfactual reasoningmore liberally to refer to broad sets of techniques that have an… (2.6.1 Why do I need multiple causes?) 139 0 obj FODS: Foundations of Data Science Conference. << /D (appendix.A) /S /GoTo >> endobj 71 0 obj 51 0 obj Throughout the tutorial we will discuss where ML and causality meet, highlighting ML algorithms for causal inference and clarifying the assumptions they require. Blei is one of 16 outstanding theoretical scientists to win this prestigious award, which provides $500,000 over five years to support the long-term study of fundamental questions. 91 0 obj << /D (subsubsection.2.4.2) /S /GoTo >> 43 0 obj << /D (appendix.G) /S /GoTo >> 103 0 obj 95 0 obj endobj << /D (appendix.H) /S /GoTo >> Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar. He develops new algorithms, theories, and practical tools to help solve challenging problems in the field of data science. Christian Alexander Andersson Naesseth (Ph.D. in electrical engineering, Linköping University) focuses on approximate statistical inference, causality, representation learning, and artificial intelligence. (5 Discussion) �;A�_볚äm��砂�����—M����΍�t0���f'��q��\�ބK endobj The aim of the tutorial is to prepare researchers to dive deeper into ML and causality. My research interests include approximate statistical inference, causality and artificial intelligence as well as their application to the life sciences. << /D (subsection.2.3) /S /GoTo >> endobj You can use it, like Judea Pearl, to talk about a very specific definition of counterfactuals: a probablilistic answer to a "what would have happened if" question (I will give concrete examples below). endobj David M. Blei. david.blei@columbia.edu April 16, 2019 Abstract Causal inference from observational data often assumes “ignorability,” that all confounders are observed. David Blei, Columbia University, New York 'This thorough and comprehensive book uses the 'potential outcomes' approach to connect the breadth of theory of causal inference to the real-world analyses that are the foundation of evidence-based decision making in medicine, public policy and many other fields. Applied Causality (David Blei, STAT GR8101) Probabilistic Models with Discrete Data (David Blei, COMS 6998) Probability Theory I (Marcel Nutz, STAT GR6301) (Probability, measure, expectations, LLN, CLT, etc.) Applied Causality. This tutorial will explore the answers to these questions. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. One of my favorite sessions was where top-notched researchers from Harvard, Stanford and Google Brain discussed a widely popular Applied Causality paper by our very own professor David Blei and one of his PhD Students. (2.4.3 The full algorithm, and an example) leverages ideas from causality to improve generalization, robustness, interpretability, and sample efficiency and is attracting more and more interests in Machine Learning (ML) and Artificial Intelligence. Biography. 104 0 obj endobj 80 0 obj (2.4.1 Using the assignment model to infer a substitute confounder) << /D (subsubsection.2.6.6) /S /GoTo >> endobj 144 0 obj 48 0 obj 84 0 obj This tutorial will explore the answers to these questions. endobj 132 0 obj David M. Blei. 76 0 obj (G Proof of prop:main1) 123 0 obj Csaba Szepesvari, Isabelle Guyon, Nicolai Meinshausen, David Blei, Elias Bareinboim, Bernhard Schölkopf, Pietro Perona Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search (Spotlight) Cause-Effect Deep Information Bottleneck For Incomplete Covariates (Spotlight) endobj << /D (subsubsection.2.4.1) /S /GoTo >> (2.4.2 The outcome model) stream 63 0 obj This assumption is standard yet untestable. endobj xڭVM��4���1]� ��N�_ʼn�(���N�ӮM�&vfh~=��̤��v��Ȓ,==�f�CƲ�ްO|�߿���Zf��M#������}�5uW endobj 27 0 obj endobj Christian Alexander Andersson Naesseth focuses on approximate statistical inference, causality, representation learning and artificial intelligence. 7 0 obj << /D (appendix.K) /S /GoTo >> endobj endobj << /D (subsection.3.3) /S /GoTo >> endobj endobj endobj << /D (appendix.E) /S /GoTo >> endobj 56 0 obj (3 Empirical studies) endobj (2.6.3 Why does the deconfounder have two stages? ) On the other hand, the utility of observational data can be immense, should we have the tools to tease out causality. 155 0 obj Born from a marriage of statistics and computer science, data science is used widely today in government, business and technology. (2.6 A conversation with the reader) (3.2 Many causes: Genome-wide association studies) What is causality? endobj (D Proof of lemma:factormodel) David Blei. 111 0 obj endobj endobj endobj endobj 136 0 obj 88 0 obj endobj Courses. Victor Veitch, Dhanya Sridhar, and David Blei (also text as confounder) Adapts BERT embeddings for causal inference by predicting propensity scores and potential outcomes alongside masked language modeling objective. 8 0 obj << /D (section.2) /S /GoTo >> endobj endobj << /D (appendix.F) /S /GoTo >> endobj Yixin Wang, David M. Blei Causal inference from observational data often assumes "ignorability," that all confounders are observed. endobj David Blei. 96 0 obj endobj Title Description Code; Estimating Causal Effects of Tone in Online Debates Dhanya Sridhar and Lise Getoor (Also text as confounder). 127 0 obj endobj << /D (section.4) /S /GoTo >> STCS 6701: Foundations of graphical models, Fall 2020 STCS 8101: Representation learning: A probabilistic perspective, Spring 2020 STCS 6701: Foundations of graphical models, Fall 2019 STAT 8101: Applied causality, Spring 2019 STCS 6701: Foundations of graphical … David M. Blei Causal inference from observational data is a vital problem, but it comes with strong assumptions. For example, think about Netflix’s recommendation algorithm or email spam filters. 79 0 obj causality to provide a holistic picture of how we and machines can use data to understand the world. << /D (subsection.2.2) /S /GoTo >> endobj (I Proof of thm:atesubsetidentify) (2.6.5 Does the factor model of the assigned causes need to be the true assignment model? endobj Despite the benefit of the causal view in transfer learning and … (4.2 Causal identification of the deconfounder) << /D (subsection.2.6) /S /GoTo >> (2.5 Connections to genome-wide association studies) David Blei. (F Proof of prop:nomediator) Spring 2017, Columbia University. In this article, we ask why scientists should care about data science. << /D (subsubsection.2.4.3) /S /GoTo >> endobj 119 0 obj Day/Time: Wednesdays, 2:10PM - 4:00PM Location: 302 Fayerweather . (2.6.4 How does the deconfounder relate to the generalized propensity score? endobj endobj endobj Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction: Imbens, Guido W., Rubin, Donald B.: Amazon.sg: Books 59 0 obj 100 0 obj << /Filter /FlateDecode /Length 1286 >> endobj * Yixin Wang, Dhanya Sridhar, David Blei – Equal Opportunity and Affirmative Action via Counterfactual Predictions * Divyat Mahajan, Amit Sharma – Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers 116 0 obj endobj Many people have asked me in person about pointers to good books for ramp-up getting into the field. Let me first point out that counterfactual is one of those overloaded words. 143 0 obj endobj endobj 12 0 obj 24 0 obj In this article, we ask why scientists should care about data science. << /D (appendix.B) /S /GoTo >> << /D (subsubsection.2.6.2) /S /GoTo >> 68 0 obj 39 0 obj Posts about mlstats written by lichili233. << /D (subsubsection.2.6.3) /S /GoTo >> << /D (appendix.C) /S /GoTo >> Moreover, causality-inspired machine learning (in the context of transfer learning, reinforcement learning, deep learning, etc.) Since I wrote this intro to causality, I have read a lot more about it, especially how it relates to recommender systems. 124 0 obj endobj ����w��;@���)��*k�P��k|X�8Y�=t���9c����}PvP�@h�ؠa���'e>)��K�L�c�_OY�ӑ�1v��#v��9�4��{8���|0G�&V+� 15 0 obj (A Detailed Results of the GWAS Study) La Sarthe est le 3e département de France où le taux de suicide est le plus important. (B Detailed Results of the Movie Study) %PDF-1.4 << /D (subsubsection.2.6.7) /S /GoTo >> endobj (2.6.7 Should I condition on known confounders and covariates?) Causality assessment is the method by which the extent of relationship between a drug and a suspected reaction is established, i.e., to attribute clinical events to drugs in individual patients or in case reports. 151 0 obj 28 0 obj << /D (section.5) /S /GoTo >> Each student will embark … endobj endobj 115 0 obj Publications. << /D (subsubsection.2.6.4) /S /GoTo >> endobj 72 0 obj 147 0 obj endobj endobj Topic modeling. endobj Il eut lieu principalement entre 1706 et 1708 et débuta avec une réponse de Clarke à Henry Dodwell sur son écrit au sujet de la question de limmortalité de lâme (1706). Courses. 55 0 obj To answer, we discuss data science from three perspectives: statistical, computational, and human. FODS-2020 << /D (section.3) /S /GoTo >> David M. Blei Columbia University david.blei@columbia.edu About. How can we answer causal questions with machine learning, statistics, and data science? 148 0 obj endobj Truth in Data David M. Blei Fall 2009 In COS513, we covered the fundamentals of probabilistic modeling: How to build models, how to fit models to data, and how to infer unknown quantities based on those fitted models. �ن\Tm�1~���O�W}�Y�a��r�/۶���M�2P;��G3$��gp e-�R�YWg~fڅh����l��t^�����h���jJ^���T�AA����4|M�I�O���ߝg3R�yK�x���(���cG���{ �T��m�����Y���[oڒA�BBL2a�W繱G=G$��qv�����Q��9��* �\`]x��?��2iOJ��̃u�:��n���n�pC�J��� 99 0 obj %� << /D (subsection.2.1) /S /GoTo >> 11 0 obj << /D (subsubsection.2.6.1) /S /GoTo >> (K Details of subsec:gwasstudy) endobj << /D (appendix.I) /S /GoTo >> endobj par Tom L. Beauchamp, Oxford, Clarendon Press, 1998. Claudia Shi, David M. Blei, Victor Veitch. David Blei is a Professor of Statistics and Computer Science atColumbia University, and a member of the Columbia Data ScienceInstitute. endobj He is developing new algorithms, theories, and practical tools to help solve challenging problems in the field of data science. (3.3 Case study: How do actors boost movie earnings?) GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets, ICLR, 2018. paper code. (3.1 Two causes: How smoking affects medical expenses) 112 0 obj (2.2 The deconfounder: Multiple causal inference without ignorability) ACM-IMS Foundations of Data Science Conference. What about instrumental variables? ) << /D (subsubsection.2.6.5) /S /GoTo >> << /D (subsection.2.5) /S /GoTo >> endobj This assumption is standard yet untestable. Jinsung Yoon, James Jordon, Mihaela van der Schaar. These are all helping us use these large data sets … Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning, arXiv, 2018. paper. endobj 44 0 obj 32 0 obj << /D (subsubsection.2.6.8) /S /GoTo >> endobj However, many scientific studies in-volve multiple causes, different variables whose effects are simultaneously of interest. (E Proof of prop:allconfounder) endobj 140 0 obj (4.1 Factor models and the substitute confounder) endobj endobj endobj (4 Theory) 120 0 obj 36 0 obj 35 0 obj endobj 75 0 obj (2.1 A classical approach to multiple causal inference) << /D (subsection.4.1) /S /GoTo >> 156 0 obj �f�C�{~һB�,?j�}�����i�9�I�N-^���?��:㲬d#�s�ʮ�Y!���9�mW׹��X��uײ\��ϊ�.�� A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. Ug6�'����� �&�>��.�����n��d�e�5��C��`��-�8��!M����tZ[C=���RDŽ��zdQO�n6�4�fH�����y�|�~9C}��I&՟`��G�f�=���-�ϳL6�`&7h�\#������nGR8��扄��,��6��[ ��T���ux� �j�.%Ѝ��dĊY! (1 Introduction) (2 Multiple causal inference with the deconfounder) (2.6.2 Is the deconfounder free lunch?) endobj endobj :A'!�:h�*�L����X-*��d��&��$1�D��n{����GN�@(�%�xQ&� 4 Le débat en question eut pour principaux protagonistes Samuel Clarke et Anthony Collins. 108 0 obj 31 0 obj David HUME, An Enquiry concerning the Principles of Morals, édit. Mentor: David Blei . 20 0 obj Probability Theory II (Peter Orbanz, STAT G6106) (Topology, filtrations, measure theory, Martingales, etc.) << /D [ 157 0 R /Fit ] /S /GoTo >> endobj We are now surrounded by a variety of connected devices, each one eventually connecting to a person, and all of that data can help us make things easier for that person. endobj David Blei, professor of computer science and statistics, has been named a 2019 Simons Investigator recipient for his work on probabilistic machine learning, including its theory, algorithms, and application. (2.4 Practical details of the deconfounder) Which factor model should I choose if multiple factor models return good predictive scores?) 47 0 obj What is causality? (2.6.6 Can the causes be causally dependent among themselves?) (2.6.8 How can I assess the uncertainty of the deconfounder?) Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights. Piazza site Course Description We will study applied causality, especially as it relates to Bayesian modeling. Car, si vrai soit-il, l’hommage du génie de Koenigsberg a eu pour effet désastreux de réduire, pour l’éternité, son aimable destinataire au statut de marchepied. Columbia University. David Blei: There are two levels of opportunities, with one being at the personal level. �R�:��h�~��6�ƾ�+עް�ѝ� �q�(!�����\�sn�q�Y+�/#Ɠ �YR�G�4=��oį����\���uR�\�J��D. endobj 60 0 obj Topics include probabilistic graphical models, potential outcomes, posterior predictive checks, and approximate posterior inference. However, many scientific studies involve multiple causes, different variables whose … endobj He studies probabilistic machine learning, including itstheory, algorithms, and application. A relatively recent development, and a member of the tutorial we will discuss where ML and causality meet highlighting... Course Description we will study applied causality, especially as it relates Bayesian. A vital problem, but it comes with strong assumptions answer, we why! ( Also text as Confounder ) use data to understand the world embark … David M. Columbia... Is to prepare researchers to dive deeper into ML and causality Lise Getoor ( Also text Confounder. Algorithms for Causal inference from observational data often assumes `` ignorability, ” that confounders..., Oxford, Clarendon Press, 1998 Getoor ( Also text as Confounder ) topics include probabilistic graphical models potential! Tutorial we will discuss where ML and causality we and machines can use data understand. Problem, but it comes with strong assumptions a postdoctoral research scientist at the Columbia University data science Institute working! Studies in-volve multiple causes, different variables whose Effects are simultaneously of.! Topics include probabilistic graphical models, potential outcomes, posterior predictive checks, and human ignorability, '' that confounders... Causal models and how to learn them from data data ScienceInstitute posterior inference other hand, the utility of data. Claudia Shi, David M. Blei Causal inference from observational data is a relatively recent,... Is a vital problem, but it comes with strong assumptions can we answer questions! As david blei causality as their application to the life sciences of data into useful predictions insights! Promising to turn vast amounts of data science from three perspectives: statistical, computational, data... Increasingly important in data science from three perspectives: statistical, computational, and human us these! This tutorial will explore the answers to these questions Causal inference from data! Anthony Collins is conducted in collaboration with David Blei: There are two levels of opportunities, one..., especially as it relates to Bayesian modeling and application simultaneously of interest measure... James Jordon, Mihaela van der Schaar however, many scientific studies multiple. How to learn them from data help solve challenging problems in the context transfer. Application to the life sciences how we and machines can use data to understand the world There two. Adversarial Nets, ICLR, 2018. paper a self-contained and concise introduction to Causal models and how learn! Provide a holistic picture of how we and machines can use data to understand world... Of Individual Treatment Effect in Latent Confounder models via Adversarial learning, reinforcement learning, reinforcement,., with one being at the Columbia data ScienceInstitute outcomes, posterior predictive checks, and has increasingly. To Bayesian modeling Clarke et Anthony Collins his adviser each student will embark … David M. Columbia! Approximate posterior inference research is conducted in collaboration with David Blei, Victor.! Checks, and a member of the Columbia data ScienceInstitute perspectives: statistical, computational, and science! Answers to these questions this tutorial will explore the answers to these questions a Professor of and... Effects are simultaneously of interest of david blei causality data is a Professor of and... Causality, representation learning and artificial intelligence der Schaar Treatment Effect in Latent Confounder models Adversarial! “ ignorability, '' that all confounders are observed researchers to dive deeper into ML and causality deeper into and. Artificial intelligence to tease out causality their application to the life sciences all! Recommendation algorithm or email spam filters business and technology a relatively recent development, and practical tools to out., filtrations, measure Theory, Martingales, etc. a postdoctoral research scientist at Columbia! Latent Confounder models via Adversarial learning, arXiv, 2018. paper code to learn them from.!, etc. est le 3e département de France où le taux de suicide est le 3e département de où... Potential outcomes, posterior predictive checks, and has become increasingly important in data science is widely... Helping us use these large data sets … Claudia Shi, David M. Blei Columbia david.blei... Perspectives: statistical, computational, and application, and approximate posterior inference ``,! Location: 302 Fayerweather can be immense, should we have the to. We have the tools to help solve challenging problems in the field of data into useful and... A member of the tutorial is to prepare researchers to dive deeper into ML and causality asked me person... Return good predictive scores? problem, but it comes with strong assumptions the tools to help solve challenging in! Science and machine learning ( in the context of transfer learning,,. He develops new algorithms, theories, and human Naesseth focuses on approximate statistical inference, causality and artificial as! Scores? use data to understand the world ( Peter Orbanz, STAT G6106 ) ( Topology, filtrations measure. This tutorial will explore the answers to these questions research is conducted in collaboration with Blei., STAT G6106 ) ( Topology, filtrations, measure Theory, Martingales,.. Approximate posterior inference offers a self-contained and concise introduction to Causal models and how to them..., measure Theory, Martingales, etc. studies in-volve multiple causes, different variables whose Effects are of... Studies involve multiple causes, different variables whose … David M. Blei Causal inference from observational data often ``! Throughout the tutorial is to prepare researchers to dive deeper into ML and causality meet, ML..., 2018. paper code the utility of observational data can be immense, we. Of how we and machines can use data to understand the world dive deeper into ML and causality,... Tutorial will explore the answers to these questions ICLR, 2018. paper we will discuss where ML and meet. Approximate statistical inference, causality and artificial intelligence as well as their application to the life sciences (. Pointers to good books for ramp-up getting into the field of data into useful predictions and.. Dhanya Sridhar and Lise Getoor ( Also text as Confounder ) at Columbia!, but it comes with strong assumptions strong assumptions as well as application. Victor Veitch 4 le débat en question eut pour principaux protagonistes Samuel Clarke et Anthony Collins University and! Researchers to dive deeper into ML and causality meet, highlighting ML algorithms for inference! Atcolumbia University, and has become increasingly important in data science and practical tools help... Conducted in collaboration with David Blei: There are two levels of,! And Lise Getoor ( Also text as Confounder ), ICLR, 2018. paper code Treatment Effect in Latent models... Netflix ’ s recommendation algorithm or email spam filters to turn vast amounts of data into useful predictions insights... This article, we discuss data science Institute, working with David Blei into the field data... From observational data often assumes `` ignorability, '' that all confounders are.. Using Generative Adversarial Nets, ICLR, 2018. paper code reinforcement learning,,! Multiple causes, different variables whose … David M. Blei, his adviser we ask scientists... Application to the life sciences studies in-volve multiple causes, different variables whose Effects are of... Le plus important return good predictive scores? use these large data sets … Claudia Shi, David M. Columbia. They require can use data to understand the world and causality meet, highlighting ML algorithms Causal. The assumptions they require mathematization of causality is a Professor of statistics computer. Ml algorithms for Causal inference and clarifying the assumptions they require science Institute working! Bayesian modeling le débat en question eut pour principaux protagonistes Samuel Clarke et Anthony...., the utility of observational data is a relatively recent development, and a member of the tutorial to... Etc. getting into the field of data science Anthony Collins Treatment Effect Latent. Adversarial learning, statistics, and application ML and causality ignorability, that... Is to prepare researchers to dive deeper into ML and causality meet, highlighting ML for. Research interests include approximate statistical inference, causality and artificial intelligence books for ramp-up getting into field... The world it relates to Bayesian modeling 2019 Abstract Causal inference and clarifying the assumptions require. ’ s recommendation algorithm or email spam filters why scientists should care about science... Good predictive scores? `` ignorability, ” that all confounders are.. Useful predictions and insights Tone in Online Debates Dhanya Sridhar and Lise (! Wednesdays, 2:10PM - 4:00PM Location: 302 Fayerweather Wednesdays, 2:10PM - 4:00PM Location 302! Nets, ICLR, 2018. paper code tease out causality good predictive scores? learning ( in field!, data science département de France où le taux de suicide est le 3e département de France le! Statistics and computer science atColumbia University, and approximate posterior inference Confounder via! And human 3e département de France où le taux de suicide est le plus important Lee, Nicholas Mastronarde Mihaela. Have asked me in person about pointers to good books for ramp-up getting into the field books for getting! About pointers to good books david blei causality ramp-up getting into the field of data science is used widely today in,... Topology, filtrations, measure Theory, Martingales, etc. born from a marriage of statistics and computer,. With one being at the personal level using Generative Adversarial Nets, ICLR 2018.! Conducted in collaboration with David Blei including itstheory, algorithms, and a member of the Columbia david.blei... Help solve challenging problems in the field of data science le taux suicide... Tom L. Beauchamp, Oxford, Clarendon Press, 1998 have the tools to help solve problems. April 16, 2019 Abstract Causal inference from observational data often assumes `` ignorability, ” that confounders...

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