Artificial Intelligence Is Stupid and Causal Reasoning Won't Fix It
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A Philosophical Analysis of Causality in Econometrics
A Philosophical Analysis of Causality in Econometrics Damien James Fennell London School of Economics and Political Science Thesis submitted to the University of London for the completion of the degree of a Doctor of Philosophy August 2005 1 UMI Number: U209675 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dissertation Publishing UMI U209675 Published by ProQuest LLC 2014. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 Abstract This thesis makes explicit, develops and critically discusses a concept of causality that is assumed in structural models in econometrics. The thesis begins with a development of Herbert Simon’s (1953) treatment of causal order for linear deterministic, simultaneous systems of equations to provide a fully explicit mechanistic interpretation for these systems. Doing this allows important properties of the assumed causal reading to be discussed including: the invariance of mechanisms to intervention and the role of independence in interventions. This work is then extended to basic structural models actually used in econometrics, linear models with errors-in-the-equations. This part of the thesis provides a discussion of how error terms are to be interpreted and sets out a way to introduce probabilistic concepts into the mechanistic interpretation set out earlier. -
ABSTRACT CAUSAL PROGRAMMING Joshua Brulé
ABSTRACT Title of dissertation: CAUSAL PROGRAMMING Joshua Brul´e Doctor of Philosophy, 2019 Dissertation directed by: Professor James A. Reggia Department of Computer Science Causality is central to scientific inquiry. There is broad agreement on the meaning of causal statements, such as \Smoking causes cancer", or, \Applying pesticides affects crop yields". However, formalizing the intuition underlying such statements and conducting rigorous inference is difficult in practice. Accordingly, the overall goal of this dissertation is to reduce the difficulty of, and ambiguity in, causal modeling and inference. In other words, the goal is to make it easy for researchers to state precise causal assumptions, understand what they represent, understand why they are necessary, and to yield precise causal conclusions with minimal difficulty. Using the framework of structural causal models, I introduce a causation coeffi- cient as an analogue of the correlation coefficient, analyze its properties, and create a taxonomy of correlation/causation relationships. Analyzing these relationships provides insight into why correlation and causation are often conflated in practice, as well as a principled argument as to why formal causal analysis is necessary. Next, I introduce a theory of causal programming that unifies a large number of previ- ously separate problems in causal modeling and inference. I describe the use and implementation of a causal programming language as an embedded, domain-specific language called `Whittemore'. Whittemore permits rigorously identifying and esti- mating interventional queries without requiring the user to understand the details of the underlying inference algorithms. Finally, I analyze the computational com- plexity in determining the equilibrium distribution of cyclic causal models. -
Causal Screening to Interpret Graph Neural
Under review as a conference paper at ICLR 2021 CAUSAL SCREENING TO INTERPRET GRAPH NEURAL NETWORKS Anonymous authors Paper under double-blind review ABSTRACT With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. However, current works on feature attribution, which frame explanation generation as attributing a prediction to the graph features, mostly focus on the statistical interpretability. They may struggle to distinguish causal and noncausal effects of features, and quantify redundancy among features, thus resulting in unsatisfactory explanations. In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect. It incrementally selects a graph feature (i.e., edge) with large causal attribution, which is formulated as the individual causal effect on the model outcome. As a model-agnostic tool, Causal Screening can be used to generate faithful and concise explanations for any GNN model. Further, by conducting extensive experiments on three graph classification datasets, we observe that Causal Screening achieves significant improvements over state-of-the-art approaches w.r.t. two quantitative metrics: predictive accuracy, contrastivity, and safely passes sanity checks. 1 INTRODUCTION Graph neural networks (GNNs) (Gilmer et al., 2017; Hamilton et al., 2017; Velickovic et al., 2018; Dwivedi et al., 2020) have exhibited impressive performance in a wide range of tasks. Such a success comes from the powerful representation learning, which incorporates the graph structure with node and edge features in an end-to-end fashion. With the growing interest in GNNs, the explainability of GNN is attracting considerable attention. -
The Thought Experiments Are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding
Georgia State University ScholarWorks @ Georgia State University Philosophy Theses Department of Philosophy Summer 8-13-2013 The Thought Experiments are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding Toni S. Adleberg Georgia State University Follow this and additional works at: https://scholarworks.gsu.edu/philosophy_theses Recommended Citation Adleberg, Toni S., "The Thought Experiments are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding." Thesis, Georgia State University, 2013. https://scholarworks.gsu.edu/philosophy_theses/141 This Thesis is brought to you for free and open access by the Department of Philosophy at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Philosophy Theses by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. THE THOUGHT EXPERIMENTS ARE RIGGED: MECHANISTIC UNDERSTANDING INHIBITS MENTALISTIC UNDERSTANDING by TONI ADLEBERG Under the Direction of Eddy Nahmias ABSTRACT Many well-known arguments in the philosophy of mind use thought experiments to elicit intuitions about consciousness. Often, these thought experiments include mechanistic explana- tions of a systems’ behavior. I argue that when we understand a system as a mechanism, we are not likely to understand it as an agent. According to Arico, Fiala, Goldberg, and Nichols’ (2011) AGENCY Model, understanding a system as an agent is necessary for generating the intuition that it is conscious. Thus, if we are presented with a mechanistic description of a system, we will be very unlikely to understand that system as conscious. Many of the thought experiments in the philosophy of mind describe systems mechanistically. I argue that my account of consciousness attributions is preferable to the “Simplicity Intuition” account proposed by David Barnett (2008) because it is more explanatory and more consistent with our intuitions. -
Introduction to Philosophy Fall 2019—Test 3 1. According to Descartes, … A. What I Really Am Is a Body, but I Also Possess
Introduction to Philosophy Fall 2019—Test 3 1. According to Descartes, … a. what I really am is a body, but I also possess a mind. b. minds and bodies can’t causally interact with one another, but God fools us into believing they do. c. cats and dogs have immortal souls, just like us. d. conscious states always have physical causes, but never have physical effects. e. what I really am is a mind, but I also possess a body. 2. Which of the following would Descartes agree with? a. We can conceive of existing without a body. b. We can conceive of existing without a mind. c. We can conceive of existing without either a mind or a body. d. We can’t conceive of mental substance. e. We can’t conceive of material substance. 3. Substance dualism is the view that … a. there are two kinds of minds. b. there are two kinds of “basic stuff” in the world. c. there are two kinds of physical particles. d. there are two kinds of people in the world—those who divide the world into two kinds of people, and those that don’t. e. material substance comes in two forms, matter and energy. 4. We call a property “accidental” (as opposed to “essential”) when ... a. it is the result of a car crash. b. it follows from a thing’s very nature. c. it is a property a thing can’t lose (without ceasing to exist). d. it is a property a thing can lose (without ceasing to exist). -
Part IV: Reminiscences
Part IV: Reminiscences 31 Questions and Answers Nils J. Nilsson Few people have contributed as much to artificial intelligence (AI) as has Judea Pearl. Among his several hundred publications, several stand out as among the historically most significant and influential in the theory and practice of AI. With my few pages in this celebratory volume, I join many of his colleagues and former students in showing our gratitude and respect for his inspiration and exemplary career. He is a towering figure in our field. Certainly one key to Judea’s many outstanding achievements (beyond dedication and hard work) is his keen ability to ask the right questions and follow them up with insightful intuitions and penetrating mathematical analyses. His overarching question, it seems to me, is “how is it that humans can do so much with simplistic, unreliable, and uncertain information?” The very name of his UCLA laboratory, the Cognitive Systems Laboratory, seems to proclaim his goal: understanding and automating the most cognitive of all systems, namely humans. In this essay, I’ll focus on the questions and inspirations that motivated his ground-breaking research in three major areas: heuristics, uncertain reasoning, and causality. He has collected and synthesized his work on each of these topics in three important books [Pearl 1984; Pearl 1988; Pearl 2000]. 1 Heuristics Pearl is explicit about what inspired his work on heuristics [Pearl 1984, p. xi]: The study of heuristics draws its inspiration from the ever-amazing ob- servation of how much people can accomplish with that simplistic, un- reliable information source known as intuition. -
Awards and Distinguished Papers
Awards and Distinguished Papers IJCAI-15 Award for Research Excellence search agenda in their area and will have a first-rate profile of influential re- search results. e Research Excellence award is given to a scientist who has carried out a e award is named for John McCarthy (1927-2011), who is widely rec- program of research of consistently high quality throughout an entire career ognized as one of the founders of the field of artificial intelligence. As well as yielding several substantial results. Past recipients of this honor are the most giving the discipline its name, McCarthy made fundamental contributions illustrious group of scientists from the field of artificial intelligence: John of lasting importance to computer science in general and artificial intelli- McCarthy (1985), Allen Newell (1989), Marvin Minsky (1991), Raymond gence in particular, including time-sharing operating systems, the LISP pro- Reiter (1993), Herbert Simon (1995), Aravind Joshi (1997), Judea Pearl (1999), Donald Michie (2001), Nils Nilsson (2003), Geoffrey E. Hinton gramming languages, knowledge representation, commonsense reasoning, (2005), Alan Bundy (2007), Victor Lesser (2009), Robert Anthony Kowalski and the logicist paradigm in artificial intelligence. e award was estab- (2011), and Hector Levesque (2013). lished with the full support and encouragement of the McCarthy family. e winner of the 2015 Award for Research Excellence is Barbara Grosz, e winner of the 2015 inaugural John McCarthy Award is Bart Selman, Higgins Professor of Natural Sciences at the School of Engineering and Nat- professor at the Department of Computer Science, Cornell University. Pro- ural Sciences, Harvard University. Professor Grosz is recognized for her pio- fessor Selman is recognized for expanding our understanding of problem neering research in natural language processing and in theories and applica- complexity and developing new algorithms for efficient inference. -
Relative Entropy, Probabilistic Inference and AI
I I RELATIVE ENTROPY, PROBABILISTIC INFERENCE, AND AI John E. Shore I Computer Science and Systems Branch Code 7591 Information Technology Division I Naval Research Laboratory Washington, D. C. 20375 I I. INTRODUCTION For probability distributions q and p, the relative entropy I n "" q · H(q,p) = L.Jqilog -'. (1) i=l p, I is an information-theoretic measure of the dissimilarity between q = q 1, • • · ,qn and p = p1 1 • • • , pn (H is also called cross-entropy, discrimination information, directed divergence, !-divergence, K-L number, among other terms). Various properties of relative entropy have led to its widespread use in information theory. These properties suggest that relative entropy has a role I to play in systems that attempt to perform inference in terms of probability distributions. In this paper, I will review some basic properties of relative entropy as well as its role in probabilistic inference. I will also mention briefly a few existing and potential applications of relative entropy I to so-called artificial intelligence (AI). I II. INFORMATION, ENTROPY, AND RELATIVE-ENTROPY A. Information and Entropy Suppose that some proposition Xi has probability p(xi) of being true. Then the amount of I information that we would obtain if we learn that Xi is true is I ( xi) = - log p ( xi) . (2) !(xi) measures the uncertainty of xi. Except for the base of the logarithm, this common I definition arises essentially from the requirement that the information implicit in two independent propositions be additive. In particular, if xi and xi, i =I=j, are independent propositions with joint probability p(xi/\ x1) = p(xi )p (x; ), then the requirements I !(xi/\ xi)= !(xi)+ !(xi) (3) and I I(xd � 0 (4) are sufficient to establish (2) except for the arbitrary base of the logarithm, which determines the units. -
Preston, John, & Bishop, Mark
Preston, John, & Bishop, Mark (2002), Views into the Chinese Room: New Essays on Searle and Artificial Intelligence (Oxford: Oxford University Press), xvi + 410 pp., ISBN 0-19-825057-6. Reviewed by: William J. Rapaport Department of Computer Science and Engineering, Department of Philosophy, and Center for Cognitive Science, State University of New York at Buffalo, Buffalo, NY 14260-2000; [email protected], http://www.cse.buffalo.edu/ rapaport ∼ This anthology’s 20 new articles and bibliography attest to continued interest in Searle’s (1980) Chinese Room Argument. Preston’s excellent “Introduction” ties the history and nature of cognitive science, computation, AI, and the CRA to relevant chapters. Searle (“Twenty-One Years in the Chinese Room”) says, “purely . syntactical processes of the implemented computer program could not by themselves . guarantee . semantic content . essential to human cognition” (51). “Semantic content” appears to be mind-external entities “attached” (53) to the program’s symbols. But the program’s implementation must accept these entities as input (suitably transduced), so the program in execution, accepting and processing this input, would provide the required content. The transduced input would then be internal representatives of the external content and would be related to the symbols of the formal, syntactic program in ways that play the same roles as the “attachment” relationships between the external contents and the symbols (Rapaport 2000). The “semantic content” could then just be those mind-internal -
Digital Culture: Pragmatic and Philosophical Challenges
DIOGENES Diogenes 211: 23–39 ISSN 0392-1921 Digital Culture: Pragmatic and Philosophical Challenges Marcelo Dascal Over the coming decades, the so-called telematic technologies are destined to grow more and more encompassing in scale and the repercussions they will have on our professional and personal lives will become ever more accentuated. The transforma- tions resulting from the digitization of data have already profoundly modified a great many of the activities of human life and exercise significant influence on the way we design, draw up, store and send documents, as well as on the means used for locating information in libraries, databases and on the net. These changes are transforming the nature of international communication and are modifying the way in which we carry out research, engage in study, keep our accounts, plan our travel, do our shopping, look after our health, organize our leisure time, undertake wars and so on. The time is not far off when miniaturized digital systems will be installed everywhere – in our homes and offices, in the car, in our pockets or even embedded within the bodies of each one of us. They will help us to keep control over every aspect of our lives and will efficiently and effectively carry out multiple tasks which today we have to devote precious time to. As a consequence, our ways of acting and thinking will be transformed. These new winds of change, which are blowing strongly, are giving birth to a new culture to which the name ‘digital culture’ has been given.1 It is timely that a study should be made of the different aspects and consequences of this culture, but that this study should not be just undertaken from the point of view of the simple user who quickly embraces the latest successful inno- vation, in reaction essentially to market forces. -
Artificial Intelligence Pioneer Wins A.M. Turing Award 15 March 2012
Artificial intelligence pioneer wins A.M. Turing Award 15 March 2012 said Vint Cerf, a Google executive who is considered one of the fathers of the Internet. "They have redefined the term 'thinking machine,'" said Cerf, who is also a Turing Award winner. The ACM said Pearl had created a "computational foundation for processing information under uncertainty, a core problem faced by intelligent systems. "His work serves as the standard method for handling uncertainty in computer systems, with Judea Pearl, winner of the 2011 A.M. Turing Award, is applications ranging from medical diagnosis, pictured with his wife Ruth at a Hanukkah celebration at homeland security and genetic counseling to the White House in 2007. natural language understanding and mapping gene expression data," it said. Judea Pearl, a pioneer in the field of artificial "His influence extends beyond artificial intelligence intelligence, has been awarded the prestigious and even computer science, to human reasoning 2011 A.M. Turing Award. and the philosophy of science." Pearl, 75, was being honored for "innovations that (c) 2012 AFP enabled remarkable advances in the partnership between humans and machines," the Association for Computing Machinery (ACM) said. The award, named for British mathematician Alan M. Turing and considered the "Nobel Prize in Computing," carries a $250,000 prize sponsored by computer chip giant Intel and Internet titan Google. Pearl is a professor of computer science at the University of California, Los Angeles. He is the father of Daniel Pearl, a journalist for The Wall Street Journal who was kidnapped and murdered in Pakistan in 2002. "(Judea Pearl's) accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and led to extraordinary achievements in machine learning," 1 / 2 APA citation: Artificial intelligence pioneer wins A.M. -
A Two Day ACM Turing Award Celebrations-Live Web Stream @
ACM Coordinators Department of Information Technology Dr.R.Suganya & Mr.A.Sheik Abdullah Assistant Professors, The Department of Information Technology is A Two day ACM Turing Award Department of Information Technology established in the year 1999. It offers B.Tech (Information Technology) UG Programme since 1999 and M.E. Celebrations-Live Web Stream Thiagarjar College of Engineering (Computer Science and Information Security) PG Mail id: [email protected], [email protected] programme since 2014. The Department is supported by @ TCE PH: +919150245157, +919994216590 well experienced and qualified faculty members. The Department focuses on imparting practical and project based training to student through Outcome Based 23-24th June, 2017 Thiagarajar College of Engineering Education. The Department has different Special Interest Thiagarajar College of Engineering, an autonomous govt. Groups (SIGs) such as Data Engineering, Distributed aided Institution, affiliated to the Anna University is one Systems, Mobile Technologies, Information Security and among the several educational and philanthropic Management. These SIGs work on promoting research and institutions founded by Philanthropist and Industrialist development activities. The Department has sponsored research projects supported by funding organisations like Late. Shri.Karumuttu Thiagarajan Chettiar. It was UGC, AICTE, Industry supported Enterprise Mobility Lab established in the year 1957 and granted Autonomy in the by Motorola and Data Analytics Lab by CDAC, Chennai. year 1987. TCE is funded by Central and State Governments and Management. TCE offers 8 Undergraduate Programmes, 13 Postgraduate Programmes About the Award Celebrations Organized by and Doctoral Programmes in Engineering, Sciences and Architecture. TCE is an approved QIP centre for pursuing The ACM A.M.