DOCSLIB.ORG

Causal Analysis of Agent Behavior for AI Safety

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Causal Analysis of Agent Behavior for AI Safety Causal Analysis of Agent Behavior for AI Safety Gregoire´ Deletang´ * 1 Jordi Grau-Moya * 1 Miljan Martic * 1 Tim Genewein 1 Tom McGrath 1 Vladimir Mikulik 1 Markus Kunesch 1 Shane Legg 1 Pedro A. Ortega 1 Abstract allow for investigating and uncovering the causal mecha- nisms that underlie an agent’s behavior. Such methodologies As machine learning systems become more pow- would enable analysts to explain, predict, and preempt fail- erful they also become increasingly unpredictable ure modes (Russell et al., 2015; Amodei et al., 2016; Leike and opaque. Yet, finding human-understandable et al., 2017). explanations of how they work is essential for their safe deployment. This technical report illus- This technical report outlines a methodology for investi- trates a methodology for investigating the causal gating agent behavior from a mechanistic point of view. mechanisms that drive the behaviour of artificial Mechanistic explanations deliver a deeper understanding of agents. Six use cases are covered, each addressing agency because they describe the cause-effect relationships a typical question an analyst might ask about an that govern behavior—they explain why an agent does what agent. In particular, we show that each question it does. Specifically, agent behavior ought to be studied cannot be addressed by pure observation alone, using the tools of causal analysis (Spirtes et al., 2000; Pearl, but instead requires conducting experiments with 2009; Dawid, 2015). In the methodology outlined here, ana- systematically chosen manipulations so as to gen- lysts conduct experiments in order to confirm the existence erate the correct causal evidence. of hypothesized behavioral structures of AI systems. In particular, the methodology encourages proposing simple Keywords: Agent analysis, black-box analysis, causal explanations that refer to high-level concepts (“the causal reasoning, AI safety. agent prefers green over red apples”) that abstract away the low-level (neural) inner workings of an agent. 1. Introduction Using a simulator, analysts can place pre-trained agents into test environments, recording their reactions to various inputs Unlike systems specifically engineered for solving a and interventions under controlled experimental conditions. narrowly-scoped task, machine learning systems such as The simulator provides additional flexibility in that it can, deep reinforcement learning agents are notoriously opaque. among other things, reset the initial state, run a sequence Even though the architecture, algorithms, and training data of interactions forward and backward in time, change the are known to the designers, the complex interplay between seed of the pseudo-random number generator, or spawn a these components gives rise to a black-box behavior that new branch of interactions. The collected data from the sim- is generally intractable to predict. This problem wors- ulator can then be analyzed using a causal reasoning engine ens as the field makes progress and AI agents become where researchers can formally express their assumptions arXiv:2103.03938v1 [cs.AI] 5 Mar 2021 more powerful and general. As illustrated by learning-to- by encoding them as causal probabilistic models and then learn approaches, learning systems can use their experience validate their hypotheses. Although labor-intensive, this to induce algorithms that shape their entire information- human-in-the-loop approach to agent analysis has the ad- processing pipeline, from perception to memorization to vantage of producing human-understandable explanations action (Wang et al., 2016; Andrychowicz et al., 2016). that are mechanistic in nature. Such poorly-understood systems do not come with the nec- essary safety guarantees for deployment. From a safety 2. Methodology perspective, it is therefore paramount to develop black-box methodologies (e.g. suitable for any agent architecture) that We illustrate this methodology through six use cases, se- lected so as to cover a spectrum of prototypical questions *Equal contribution 1AGI Safety Analysis, DeepMind, an agent analyst might ask about the mechanistic drivers London, UK. Correspondence to: Pedro A. Ortega <pe- of behavior. For each use case, we present a minimalis- [email protected]>. tic grid-world example and describe how we performed ©2020 by the authors. our investigation. We limit ourselves to environmental and Causal Analysis of Agent Behavior for AI Safety behavioral manipulations, but direct interventions on the internal state of agents are also possible. The simplicity in a) t t+1 our examples is for the sake of clarity only; conceptually, all solution methods carry over to more complex scenarios under appropriate experimental controls. Our approach uses several components: an agent and an environment, a simulator of interaction trajectories, and a causal reasoning engine. These are described in turn. b) W W 2.1. Agents and environments t t+1 For simplicity, we consider stateful agents and environments that exchange interaction symbols (i.e. actions and obser- At-1 Ot At Ot+1 vations) drawn from finite sets in chronological order at discrete time steps t = 1; 2; 3;::: Typically, the agent is a system that was pre-trained using reinforcement learning Mt-1 Mt Mt+1 and the environment is a partially-observable Markov de- time t cision process, such as in Figure1a. Let mt; wt (agent’s memory state, world state) and a ; o (action, observation) t t Figure 1. Agents and environments. a) The goal of the agent is to denote the internal states and interaction symbols at time t pick up a reward pill without stepping into a lava tile. b) Causal of the agent and the environment respectively. These inter- Bayesian network describing the generative process of agent- actions influence the stochastic evolution of their internal environment interactions. The environmental state Wt and the states according to the following (causal) conditional proba- agent’s memory state Mt evolve through the exchange of action bilities: and observation symbols At and Ot respectively. wt ∼ P (wt j wt−1; at−1) ot ∼ P (ot j wt) (1) system made from coupling an agent and an environment, a m ∼ P (m j m ; o ) a ∼ P (a j m ): (2) t t t−1 t t t t random seed ! ∼ P (!), and a desired length T , it generates These dependencies are illustrated in the causal Bayesian a trace network of Figure1b describing the perception-action loop τ = (!; s1; x1); (!; s2; x2); (!; s3; x3);:::; (!; sT ; xT ) (Tishby & Polani, 2011). of a desired length T , where the s := (! ; m ) and Since we wish to have complete control over the stochastic t t t x := (o ; a ) are the combined state and interaction sym- components of the interaction process (by controlling its t t t bols respectively, and where ! is the random element which random elements), we turn the above into a deterministic has been made explicit. The simulator can also contract system through a re-parameterization1. Namely, we repre- (rewind) or expand the trace to an arbitrary time point sent the above distributions using functions W; M; O; A as T 0 ≥ 1. Note that this works seamlessly as the genera- follows: tive process of the trace is deterministic. wt = W (wt−1; at−1;!) ot = O(wt;!) (3) In addition, the simulator allows for manipulations of the mt = M(mt−1; ot;!) at = A(mt;!) (4) trace. Such an intervention at time t can alter any of the three components of the triple (!; st; xt). For instance, where ! ∼ P (!) is the random seed. This re- changing the random seed in the first time step corresponds parameterization is natural in the case of agents and en- to sampling a new trajectory: vironments implemented as programs. τ = (!; s1; x1); (!; s2; x2);:::; (!; sT ; xT ) 2.2. Simulator # (5) τ 0 = (!0; s0 ; x0 ); (!0; s0 ; x0 );:::; (!0; s0 ; x0 ); The purpose of the simulator is to provide platform for exper- 1 1 2 2 T T imentation. Its primary function is to generate traces (roll- whereas changing the state at time step t = 2 produces a outs) of agent-environment interactions (Figure2). Given a new branch of the process sharing the same root: 1 That is, we describe the system as a structural causal model τ = (!; s1; x1); (!; s2; x2);:::; (!; sT ; xT ) as described in Pearl(2009, chapter 7). Although this parame- terization is chosen for the sake of concreteness, others are also # (6) 0 0 0 0 0 possible. τ = (!; s1; x1); (!; s2; x2);:::; (!; sT ; xT ): Page 2 Causal Analysis of Agent Behavior for AI Safety Rollout 1 change seed Rollout 2 change tiles Rollout 3 Figure 2. Simulating a trace (rollout) and performing interventions, creating new branches. Using these primitives one can generate a wealth of data model for this situation would be the system of equations about the behavior of the system. This is illustrated in Figure2. X = fX (UX ) UX ∼ P (UX ) Y = fY (X; UY ) UY ∼ P (UY ) (7) 2.3. Causal reasoning engine Z = fZ (X; Y; UZ ) UZ ∼ P (UZ ) Finally, in order to gain a mechanistic understanding of the agent’s behavior from the data generated by the simulator, where fX ; fY , and fZ are (deterministic) functions and it is necessary to use a formal system for reasoning about where the (exogenous) variables UX ;UY , UZ encapsulate statistical causality. The purpose of the causal reasoning the stochastic components of the model. Together, they engine is to allow analysts to precisely state and validate induce the conditional probabilities their causal hypotheses using fully automated deductive reasoning algorithms. P (X);P (Y j X); and P (Z j X; Y ): (8) As an illustration of the modeling process, consider an an- These probabilities can be directly supplied by the analyst alyst wanting to understand whether an agent avoids lava (e.g. if they denote prior probabilities over hypotheses) or when trying to reach a goal state. First, the analyst selects estimated from Monte-Carlo samples obtained from the the set of random variables X they want to use to model the simulator (see next subsection).
Load more

Recommended publications
  • A Difference-Making Account of Causation1
    A difference-making account of causation1 Wolfgang Pietsch2, Munich Center for Technology in Society, Technische Universität München, Arcisstr. 21, 80333 München, Germany A difference-making account of causality is proposed that is based on a counterfactual definition, but differs from traditional counterfactual approaches to causation in a number of crucial respects: (i) it introduces a notion of causal irrelevance; (ii) it evaluates the truth-value of counterfactual statements in terms of difference-making; (iii) it renders causal statements background-dependent. On the basis of the fundamental notions ‘causal relevance’ and ‘causal irrelevance’, further causal concepts are defined including causal factors, alternative causes, and importantly inus-conditions. Problems and advantages of the proposed account are discussed. Finally, it is shown how the account can shed new light on three classic problems in epistemology, the problem of induction, the logic of analogy, and the framing of eliminative induction. 1. Introduction ......................................................................................................................................................... 2 2. The difference-making account ........................................................................................................................... 3 2a. Causal relevance and causal irrelevance ....................................................................................................... 4 2b. A difference-making account of causal counterfactuals
    [Show full text]
  • A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications
    A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications Xiao Xie, Fan Du, and Yingcai Wu Fig. 1. The user interface of Causality Explorer demonstrated with a real-world audiology dataset that consists of 200 rows and 24 dimensions [18]. (a) A scalable causal graph layout that can handle high-dimensional data. (b) Histograms of all dimensions for comparative analyses of the distributions. (c) Clicking on a histogram will display the detailed data in the table view. (b) and (c) are coordinated to support what-if analyses. In the causal graph, each node is represented by a pie chart (d) and the causal direction (e) is from the upper node (cause) to the lower node (result). The thickness of a link encodes the uncertainty (f). Nodes without descendants are placed on the left side of each layer to improve readability (g). Users can double-click on a node to show its causality subgraph (h). Abstract—Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, arXiv:2009.02458v1 [cs.HC] 5 Sep 2020 and apply causal relations in real-world decision-making scenarios.
    [Show full text]
  • Causal Inference for Process Understanding in Earth Sciences
    Causal inference for process understanding in Earth sciences Adam Massmann,* Pierre Gentine, Jakob Runge May 4, 2021 Abstract There is growing interest in the study of causal methods in the Earth sciences. However, most applications have focused on causal discovery, i.e. inferring the causal relationships and causal structure from data. This paper instead examines causality through the lens of causal inference and how expert-defined causal graphs, a fundamental from causal theory, can be used to clarify assumptions, identify tractable problems, and aid interpretation of results and their causality in Earth science research. We apply causal theory to generic graphs of the Earth sys- tem to identify where causal inference may be most tractable and useful to address problems in Earth science, and avoid potentially incorrect conclusions. Specifically, causal inference may be useful when: (1) the effect of interest is only causally affected by the observed por- tion of the state space; or: (2) the cause of interest can be assumed to be independent of the evolution of the system’s state; or: (3) the state space of the system is reconstructable from lagged observations of the system. However, we also highlight through examples that causal graphs can be used to explicitly define and communicate assumptions and hypotheses, and help to structure analyses, even if causal inference is ultimately challenging given the data availability, limitations and uncertainties. Note: We will update this manuscript as our understanding of causality’s role in Earth sci- ence research evolves. Comments, feedback, and edits are enthusiastically encouraged, and we arXiv:2105.00912v1 [physics.ao-ph] 3 May 2021 will add acknowledgments and/or coauthors as we receive community contributions.
    [Show full text]
  • Equations Are Effects: Using Causal Contrasts to Support Algebra Learning
    Equations are Effects: Using Causal Contrasts to Support Algebra Learning Jessica M. Walker ([email protected]) Patricia W. Cheng ([email protected]) James W. Stigler ([email protected]) Department of Psychology, University of California, Los Angeles, CA 90095-1563 USA Abstract disappointed to discover that your digital video recorder (DVR) failed to record a special television show last night U.S. students consistently score poorly on international mathematics assessments. One reason is their tendency to (your goal). You would probably think back to occasions on approach mathematics learning by memorizing steps in a which your DVR successfully recorded, and compare them solution procedure, without understanding the purpose of to the failed attempt. If a presetting to record a regular show each step. As a result, students are often unable to flexibly is the only feature that differs between your failed and transfer their knowledge to novel problems. Whereas successful attempts, you would readily determine the cause mathematics is traditionally taught using explicit instruction of the failure -- the presetting interfered with your new to convey analytic knowledge, here we propose the causal contrast approach, an instructional method that recruits an setting. This process enables you to discover a new causal implicit empirical-learning process to help students discover relation and better understand how your DVR works. the reasons underlying mathematical procedures. For a topic Causal induction is the process whereby we come to in high-school algebra, we tested the causal contrast approach know how the empirical world works; it is what allows us to against an enhanced traditional approach, controlling for predict, diagnose, and intervene on the world to achieve an conceptual information conveyed, feedback, and practice.
    [Show full text]
  • Judea's Reply
    Causal Analysis in Theory and Practice On Mediation, counterfactuals and manipulations Filed under: Discussion, Opinion — moderator @ 9:00 pm , 05/03/2010 Judea Pearl's reply: 1. As to the which counterfactuals are "well defined", my position is that counterfactuals attain their "definition" from the laws of physics and, therefore, they are "well defined" before one even contemplates any specific intervention. Newton concluded that tides are DUE to lunar attraction without thinking about manipulating the moon's position; he merely envisioned how water would react to gravitaional force in general. In fact, counterfactuals (e.g., f=ma) earn their usefulness precisely because they are not tide to specific manipulation, but can serve a vast variety of future inteventions, whose details we do not know in advance; it is the duty of the intervenor to make precise how each anticipated manipulation fits into our store of counterfactual knowledge, also known as "scientific theories". 2. Regarding identifiability of mediation, I have two comments to make; ' one related to your Minimal Causal Models (MCM) and one related to the role of structural equations models (SEM) as the logical basis of counterfactual analysis. In my previous posting in this discussion I have falsely assumed that MCM is identical to the one I called "Causal Bayesian Network" (CBM) in Causality Chapter 1, which is the midlevel in the three-tier hierarchy: probability-intervention-counterfactuals? A Causal Bayesian Network is defined by three conditions (page 24), each invoking probabilities
    [Show full text]
  • Causal Analysis of COVID-19 Observational Data in German Districts Reveals Effects of Mobility, Awareness, and Temperature
    medRxiv preprint doi: https://doi.org/10.1101/2020.07.15.20154476; this version posted July 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Causal analysis of COVID-19 observational data in German districts reveals effects of mobility, awareness, and temperature Edgar Steigera, Tobias Mußgnuga, Lars Eric Krolla aCentral Research Institute of Ambulatory Health Care in Germany (Zi), Salzufer 8, D-10587 Berlin, Germany Abstract Mobility, awareness, and weather are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated their causal effects on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the aforementioned determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany’s National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable causal effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy), and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects of public mobility in retail and recreational areas, awareness measured by searches for “corona” in Google, and higher rainfall, leading to an increase in new COVID-19 cases.
    [Show full text]
  • 12.4 Methods of Causal Analysis 519
    M12_COPI1396_13_SE_C12.QXD 11/13/07 9:39 AM Page 519 12.4 Methods of Causal Analysis 519 therefore unlikely even to notice, the negative or disconfirming instances that might otherwise be found. For this reason, despite their fruitfulness and value in suggesting causal laws, inductions by simple enumeration are not at all suitable for testing causal laws. Yet such testing is essential; to accomplish it we must rely upon other types of inductive arguments—and to these we turn now. 12.4 Methods of Causal Analysis The classic formulation of the methods central to all induction were given in the nineteenth century by John Stuart Mill (in A System of Logic, 1843). His systematic account of these methods has led logicians to refer to them as Mill’s methods of inductive inference. The techniques themselves—five are commonly distinguished—were certainly not invented by him, nor should they be thought of as merely a product of nineteenth-century thought. On the contrary, these are universal tools of scientific investigation. The names Mill gave to them are still in use, as are Mill’s precise formulations of what he called the “canons of induction.” These techniques of investigation are per- manently useful. Present-day accounts of discoveries in the biological, social, and physical sciences commonly report the methodology used as one or another variant (or combination) of these five techniques of inductive infer- ence called Mill’s methods: They are 1. The method of agreement 2. The method of difference 3. The joint method of agreement and difference *Induction by simple enumeration can mislead even the learned.
    [Show full text]
  • Chapter 17 Social Networks and Causal Inference
    Chapter 17 Social Networks and Causal Inference Tyler J. VanderWeele and Weihua An Abstract This chapter reviews theoretical developments and empirical studies related to causal inference on social networks from both experimental and observational studies. Discussion is given to the effect of experimental interventions on outcomes and behaviors and how these effects relate to the presence of social ties, the position of individuals within the network, and the underlying structure and properties of the network. The effects of such experimental interventions on changing the network structure itself and potential feedback between behaviors and network changes are also discussed. With observational data, correlations in behavior or outcomes between individuals with network ties may be due to social influence, homophily, or environmental confounding. With cross-sectional data these three sources of correlation cannot be distinguished. Methods employing longitudinal observational data that can help distinguish between social influence, homophily, and environmental confounding are described, along with their limitations. Proposals are made regarding future research directions and methodological developments that would help put causal inference on social networks on a firmer theoretical footing. Introduction Although the literature on social networks has grown dramatically in recent years (see Goldenberg et al. 2009;An2011a, for reviews), formal theory for inferring causation on social networks is arguably still in its infancy. As described elsewhere in this handbook, much of the recent theoretical development within causal inference has been within a “counterfactual-” or “potential outcomes-” based perspective (Rubin 1974; see also Chap. 5 by Mahoney et al., this volume), and this approach typically employs a “no interference” assumption (Cox 1958; Rubin 1980; see also Chap.
    [Show full text]
  • Confounding and Control
    Demographic Research a free, expedited, online journal of peer-reviewed research and commentary in the population sciences published by the Max Planck Institute for Demographic Research Konrad-Zuse Str. 1, D-18057 Rostock · GERMANY www.demographic-research.org DEMOGRAPHIC RESEARCH VOLUME 16, ARTICLE 4, PAGES 97-120 PUBLISHED 06 FEBRUARY 2007 http://www.demographic-research.org/Volumes/Vol16/4/ DOI: 10.4054/DemRes.2007.16.4 Research Article Confounding and control Guillaume Wunsch © 2007 Wunsch This open-access work is published under the terms of the Creative Commons Attribution NonCommercial License 2.0 Germany, which permits use, reproduction & distribution in any medium for non-commercial purposes, provided the original author(s) and source are given credit. See http:// creativecommons.org/licenses/by-nc/2.0/de/ Table of Contents 1 Introduction 98 2 Confounding 100 2.1 What is confounding? 100 2.2 A confounder as a common cause 101 3 Control 104 3.1 Controlling ex ante: randomisation in prospective studies 104 3.2 Treatment selection in non-experimental studies 106 3.3 Controlling ex post in non-experimental studies 107 3.4 Standardisation 108 3.5 Multilevel modelling 109 3.6 To control … ? 110 3.7 Controlling for a latent confounder 110 3.8 … or not to control? 112 3.9 Common causes and competing risks 114 4 Conclusions 115 5 Acknowledgments 116 References 117 Demographic Research: Volume 16, Article 4 research article Confounding and control Guillaume Wunsch 1 Abstract This paper deals both with the issues of confounding and of control, as the definition of a confounding factor is far from universal and there exist different methodological approaches, ex ante and ex post, for controlling for a confounding factor.
    [Show full text]
  • Causal Laws and Causal Analysis
    Esc'r.r THE CAUSAL VERSION OF THE MODEL 87 uncommon for philosophers to represent causal laws as having special explanatory force. Thus C. Ducasse, after defining IV J. explanation in terms of subsumption under a 'law of con- nection', and having added that mere'law CAUSAL LAWS AND CAUSAL ANALYSIS a of correlation' will not do, goes on to say that laws of the former sort are either causal or logical.' And many contemporary philosophers of t. The Causal Versionof the Model science, with quantum physics in mind, would agree with o far I have said very little about specifically causal Mr. A. P. Ushenko that causal laws alone have "explanatory explanation.In ChapterII, althoughcausal language was virtue".2 not avoidedaltogether, our concernwas chieflytotestthe No doubt many of those who have phrased the covering law covering law claims with respect to explanations which were claim in terms of specifically causal laws have used the term complete in a special sense, and which would not necessarily, 'causal' carelessly.Some have meant no more than 'empirical or even naturally, be formulated in causal terms. In Chapter laws', by contrast with, say, general principles of logic. Others III, too, no attempt was made to contrast explanations given have probably had in mind a distinction within the class by making reference to causal laws with explanations of other of empirical laws, between mere 'probability hypotheses' or kinds. But some defenders of the model have stated their statistical generalizations and genuinely universal laws-for claims explicitly in terms of covering causal laws, as if sub- causal laws are often held to set forth invariable connexions.
    [Show full text]
  • Ch. 4: Statistical Research Designs for Causal Inference∗
    Ch. 4: Statistical research designs for causal inference∗ Fabrizio Gilardiy January 24, 2012 1 Introduction In Chapter 3 we have discussed the different ways in which the social sciences conceptualize causation and we have argued that there is no single way in which causal relationships can be defined and analyzed empirically. In this chapter, we focus on a specific set of approaches to constructing research designs for causal analysis, namely, one based on the potential outcomes framework developed in statistics. As discussed in Chapter 3, this perspective is both proba- bilistic and counterfactual. It is probabilistic because it does not assume that the presence of a given cause leads invariably to a given effect, while it is counterfactual because it involves the comparison of actual configurations with hypothetical alternatives that are not observed in reality. In essence, this approach underscores the necessity to rely on comparable groups in order to achieve valid causal inferences. An important implication is that the design of a study is of paramount importance. The way in which the data are produced is the critical step of the research, while the actual data analysis, while obviously important, plays a secondary role. However, a convincing design requires research questions to be broken down to manageable pieces. Thus, the big tradeoff in this perspective is between reliable inferences on very specific causal relationships on the one hand, and their broader context and complexity (and, possibly, theoretical relevance) on the other hand. The chapter first distinguishes between two general perspectives on causality, namely, one that puts the causes of effects in the foreground, and another that is more interested in the effects of causes.
    [Show full text]
  • Causal Inference in Statistics: an Overview∗†‡
    Statistics Surveys Vol. 3 (2009) 96–146 ISSN: 1935-7516 DOI: 10.1214/09-SS057 Causal inference in statistics: An overview∗†‡ Judea Pearl Computer Science Department University of California, Los Angeles, CA 90095 USA e-mail: [email protected] Abstract: This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that un- derly all causal inferences, the languages used in formulating those assump- tions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coher- ent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interven- tions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attri- bution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. Keywords and phrases: Structuralequation models, confounding,graph- ical methods, counterfactuals, causal effects, potential-outcome, mediation, policy evaluation, causes of effects.
    [Show full text]