DOCSLIB.ORG

Statistics and Causal Inference

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistics and Causal Inference Statistics and Causal Inference Kosuke Imai Princeton University June 2012 Empirical Implications of Theoretical Models (EITM) Summer Institute Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 1 / 82 Three Modes of Statistical Inference 1 Descriptive Inference: summarizing and exploring data Inferring “ideal points” from rollcall votes Inferring “topics” from texts and speeches Inferring “social networks” from surveys 2 Predictive Inference: forecasting out-of-sample data points Inferring future state failures from past failures Inferring population average turnout from a sample of voters Inferring individual level behavior from aggregate data 3 Causal Inference: predicting counterfactuals Inferring the effects of ethnic minority rule on civil war onset Inferring why incumbency status affects election outcomes Inferring whether the lack of war among democracies can be attributed to regime types Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 2 / 82 What is “Identification”? Inference: Learn about what you do not observe (parameters) from what you do observe (data) Identification: How much can we learn about parameters from infinite amount of data? Ambiguity vs. Uncertainty Identification assumptions vs. Statistical assumptions Point identification vs. Partial identification FURTHER READING: C. F. Manski. (2007). Identification for Prediction and Decision. Harvard University Press. Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 3 / 82 What is Causal Inference? Comparison between factual and counterfactual Incumbency effect: What would have been the election outcome if a candidate were not an incumbent? Resource curse thesis: What would have been the GDP growth rate without oil? Democratic peace theory: Would the two countries have escalated crisis in the same situation if they were both autocratic? FURTHER READING: Holland, P. (1986). Statistics and causal inference. (with discussions) Journal of the American Statistical Association, Vol. 81: 945–960. Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 4 / 82 Defining Causal Effects Units: i = 1;:::; n “Treatment”: Ti = 1 if treated, Ti = 0 otherwise Observed outcome: Yi Pre-treatment covariates: Xi Potential outcomes: Yi (1) and Yi (0) where Yi = Yi (Ti ) Voters Contact Turnout Age Party ID i Ti Yi (1) Yi (0) Xi Xi 1 1 1 ? 20 D 2 0 ? 0 55 R 3 0 ? 1 40 R . n 1 0 ? 62 D Causal effect: Yi (1) − Yi (0) Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 5 / 82 The Key Assumptions No simultaneity (different from endogeneity) No interference between units: Yi (T1; T2;:::; Tn) = Yi (Ti ) Potential violations: 1 spill-over effects 2 carry-over effects Cluster randomized experiments as a solution (more later) Stable Unit Treatment Value Assumption (SUTVA): no interference + “the same version” of the treatment Potential outcome is thought to be fixed: data cannot distinguish fixed and random potential outcomes But, potential outcomes across units have a distribution Observed outcome is random because the treatment is random Multi-valued treatment: more potential outcomes for each unit Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 6 / 82 Causal Effects of Immutable Characteristics “No causation without manipulation” (Holland, 1986) Immutable characteristics; gender, race, age, etc. What does the causal effect of gender mean? Causal effect of having a female politician on policy outcomes (Chattopadhyay and Duflo, 2004 QJE) Causal effect of having a discussion leader with certain preferences on deliberation outcomes (Humphreys et al. 2006 WP) Causal effect of a job applicant’s gender/race on call-back rates (Bertrand and Mullainathan, 2004 AER) Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 7 / 82 Average Treatment Effects Sample Average Treatment Effect (SATE): n 1 X Y (1) − Y (0) n i i i=1 Population Average Treatment Effect (PATE): E(Yi (1) − Yi (0)) Population Average Treatment Effect for the Treated (PATT): E(Yi (1) − Yi (0) j Ti = 1) Causal heterogeneity: Zero ATE doesn’t mean zero effect for everyone! Other quantities: Conditional ATE, Quantile Treatment Effects, etc. Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 8 / 82 Classical Randomized Experiments Units: i = 1;:::; n May constitute a simple random sample from a population Treatment: Ti 2 f0; 1g Outcome: Yi = Yi (Ti ) Complete randomization of the treatment assignment Exactly n1 units receive the treatment n0 = n − n1 units are assigned to the control group Pn Assumption: for all i = 1;:::; n, i=1 Ti = n1 and n (Y (1); Y (0)) ?? T ; Pr(T = 1) = 1 i i i i n Estimand = SATE or PATE Estimator = Difference-in-means: n n 1 X 1 X τ^ ≡ Ti Yi − (1 − Ti )Yi n1 n0 i=1 i=1 Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 9 / 82 Estimation of Average Treatment Effects Key idea (Neyman 1923): Randomness comes from treatment assignment (plus sampling for PATE) alone Design-based (randomization-based) rather than model-based Statistical properties of τ^ based on design features n Define O ≡ fYi (0); Yi (1)gi=1 Unbiasedness (over repeated treatment assignments): n n 1 X 1 X E(^τ j O) = E(Ti j O)Yi (1) − f1 − E(Ti j O)gYi (0) n1 n0 i=1 i=1 n 1 X = (Y (1) − Y (0)) = SATE n i i i=1 Over repeated sampling: E(^τ) = E(E(^τ j O)) = E(SATE) = PATE Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 10 / 82 Relationship with Regression The model: Yi = α + βTi + i where E(i ) = 0 Equivalence: least squares estimate β^ =Difference in means Potential outcomes representation: Yi (Ti ) = α + βTi + i Constant additive unit causal effect: Yi (1) − Yi (0) = β for all i α = E(Yi (0)) A more general representation: Yi (Ti ) = α + βTi + i (Ti ) where E(i (t)) = 0 Yi (1) − Yi (0) = β + i (1) − i (0) β = E(Yi (1) − Yi (0)) α = E(Yi (0)) as before Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 11 / 82 Bias of Model-Based Variance The design-based perspective: use Neyman’s exact variance What is the bias of the model-based variance estimator? Finite sample bias: ! ! σ^2 σ2 σ2 Bias = − 1 + 0 E Pn 2 i=1(Ti − T n) n1 n0 (n1 − n0)(n − 1) 2 2 = (σ1 − σ0) n1n0(n − 2) 2 2 Bias is zero when n1 = n0 or σ1 = σ0 In general, bias can be negative or positive and does not asymptotically vanish Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 12 / 82 Robust Standard Error 2 2 Suppose Var(i j T ) = σ (Ti ) 6= σ Heteroskedasticity consistent robust variance estimator: n !−1 n ! n !−1 \^ X > X 2 > X > Var((^α; β) j T ) = xi xi ^i xi xi xi xi i=1 i=1 i=1 where in this case xi = (1; Ti ) is a column vector of length 2 Model-based justification: asymptotically valid in the presence of heteroskedastic errors Design-based evaluation: ! σ2 σ2 = − 1 + 0 Finite Sample Bias 2 2 n1 n0 Bias vanishes asymptotically Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 13 / 82 Cluster Randomized Experiments Units: i = 1; 2;:::; nj Clusters of units: j = 1; 2;:::; m Treatment at cluster level: Tj 2 f0; 1g Outcome: Yij = Yij (Tj ) Random assignment: (Yij (1); Yij (0))??Tj Estimands at unit level: m nj 1 X X SATE ≡ (Y (1) − Y (0)) Pm n ij ij j=1 j j=1 i=1 PATE ≡ E(Yij (1) − Yij (0)) Random sampling of clusters and units Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 14 / 82 Merits and Limitations of CREs Interference between units within a cluster is allowed Assumption: No interference between units of different clusters Often easy to implement: Mexican health insurance experiment Opportunity to estimate the spill-over effects D. W. Nickerson. Spill-over effect of get-out-the-vote canvassing within household (APSR, 2008) Limitations: 1 A large number of possible treatment assignments 2 Loss of statistical power Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 15 / 82 Design-Based Inference For simplicity, assume equal cluster size, i.e., nj = n for all j The difference-in-means estimator: m m 1 X 1 X τ^ ≡ Tj Y j − (1 − Tj )Y j m1 m0 j=1 j=1 Pnj where Y j ≡ i=1 Yij =nj Easy to show E(^τ j O) = SATE and thus E(^τ) = PATE Exact population variance: Var(Y (1)) Var(Y (0)) Var(^τ) = j + j m1 m0 Intracluster correlation coefficient ρt : σ2 Var(Y (t)) = t f1 + (n − 1)ρ g ≤ σ2 j n t t Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 16 / 82 Cluster Standard Error Cluster robust variance estimator: −1 −1 0 m 1 0 m 1 0 m 1 \^ X > X > > X > Var((^α; β) j T ) = @ Xj Xj A @ Xj ^j ^j Xj A @ Xj Xj A j=1 j=1 j=1 where in this case Xj = [1Tj ] is an nj × 2 matrix and ^j = (^1j ;:::; ^nj j ) is a column vector of length nj Design-based evaluation (assume nj = n for all j): ! (Y (1)) (Y (0)) = − V j + V j Finite Sample Bias 2 2 m1 m0 Bias vanishes asymptotically as m ! 1 with n fixed Implication: cluster standard errors by the unit of treatment assignment Kosuke Imai (Princeton) Statistics & Causal Inference EITM, June 2012 17 / 82 Example: Seguro Popular de Salud (SPS) Evaluation of the Mexican universal health insurance program Aim: “provide social protection in health to the 50 million uninsured Mexicans” A key goal: reduce out-of-pocket health expenditures Sounds obvious but not easy to achieve in developing countries Individuals must affiliate in order to receive SPS services 100 health clusters nonrandomly chosen for evaluation Matched-pair design: based on population, socio-demographics, poverty, education, health infrastructure etc.
Load more

Recommended publications
  • The Practice of Causal Inference in Cancer Epidemiology
    Vol. 5. 303-31 1, April 1996 Cancer Epidemiology, Biomarkers & Prevention 303 Review The Practice of Causal Inference in Cancer Epidemiology Douglas L. Weedt and Lester S. Gorelic causes lung cancer (3) and to guide causal inference for occu- Preventive Oncology Branch ID. L. W.l and Comprehensive Minority pational and environmental diseases (4). From 1965 to 1995, Biomedical Program IL. S. 0.1. National Cancer Institute, Bethesda, Maryland many associations have been examined in terms of the central 20892 questions of causal inference. Causal inference is often practiced in review articles and editorials. There, epidemiologists (and others) summarize evi- Abstract dence and consider the issues of causality and public health Causal inference is an important link between the recommendations for specific exposure-cancer associations. practice of cancer epidemiology and effective cancer The purpose of this paper is to take a first step toward system- prevention. Although many papers and epidemiology atically reviewing the practice of causal inference in cancer textbooks have vigorously debated theoretical issues in epidemiology. Techniques used to assess causation and to make causal inference, almost no attention has been paid to the public health recommendations are summarized for two asso- issue of how causal inference is practiced. In this paper, ciations: alcohol and breast cancer, and vasectomy and prostate we review two series of review papers published between cancer. The alcohol and breast cancer association is timely, 1985 and 1994 to find answers to the following questions: controversial, and in the public eye (5). It involves a common which studies and prior review papers were cited, which exposure and a common cancer and has a large body of em- causal criteria were used, and what causal conclusions pirical evidence; over 50 studies and over a dozen reviews have and public health recommendations ensued.
    [Show full text]
  • Statistics and Causal Inference (With Discussion)
    Applied Statistics Lecture Notes Kosuke Imai Department of Politics Princeton University February 2, 2008 Making statistical inferences means to learn about what you do not observe, which is called parameters, from what you do observe, which is called data. We learn the basic principles of statistical inference from a perspective of causal inference, which is a popular goal of political science research. Namely, we study statistics by learning how to make causal inferences with statistical methods. 1 Statistical Framework of Causal Inference What do we exactly mean when we say “An event A causes another event B”? Whether explicitly or implicitly, this question is asked and answered all the time in political science research. The most commonly used statistical framework of causality is based on the notion of counterfactuals (see Holland, 1986). That is, we ask the question “What would have happened if an event A were absent (or existent)?” The following example illustrates the fact that some causal questions are more difficult to answer than others. Example 1 (Counterfactual and Causality) Interpret each of the following statements as a causal statement. 1. A politician voted for the education bill because she is a democrat. 2. A politician voted for the education bill because she is liberal. 3. A politician voted for the education bill because she is a woman. In this framework, therefore, the fundamental problem of causal inference is that the coun- terfactual outcomes cannot be observed, and yet any causal inference requires both factual and counterfactual outcomes. This idea is formalized below using the potential outcomes notation.
    [Show full text]
  • Third Year Preceptor Evaluation Form
    3rd Year Preceptor Evaluation Please select student’s home campus: Auburn Carolinas Virginia Printed Student Name: Start Date: End Date: Printed Preceptor Name and Degree: Discipline: The below performance ratings are designed to evaluate a student engaged in their 3rd year of clinical rotations which corresponds to their first year of full time clinical training. Unacceptable – performs below the expected standards for the first year of clinical training (OMS3) despite feedback and direction Below expectations – performs below expectations for the first year of clinical training (OMS3). Responds to feedback and direction but still requires maximal supervision, and continual prompting and direction to achieve tasks Meets expectations – performs at the expected level of training (OMS3); able to perform basic tasks with some prompting and direction Above expectations – performs above expectations for their first year of clinical training (OMS3); requires minimal prompting and direction to perform required tasks Exceptional – performs well above peers; able to model tasks for peers or juniors, of medical students at the OMS3 level Place a check in the appropriate column to indicate your rating for the student in that particular area. Clinical skills and Procedure Log Documentation Preceptor has reviewed and discussed the CREDO LOG (Clinical Experience) for this Yes rotation. This is mandatory for passing the rotation for OMS 3. No Area of Evaluation - Communication Question Unacceptable Below Meets Above Exceptional N/A Expectations Expectations Expectations 1. Effectively listen to patients, family, peers, & healthcare team. 2. Demonstrates compassion and respect in patient communications. 3. Effectively collects chief complaint and history. 4. Considers whole patient: social, spiritual & cultural concerns.
    [Show full text]
  • Bayesian Causal Inference
    Bayesian Causal Inference Maximilian Kurthen Master’s Thesis Max Planck Institute for Astrophysics Within the Elite Master Program Theoretical and Mathematical Physics Ludwig Maximilian University of Munich Technical University of Munich Supervisor: PD Dr. Torsten Enßlin Munich, September 12, 2018 Abstract In this thesis we address the problem of two-variable causal inference. This task refers to inferring an existing causal relation between two random variables (i.e. X → Y or Y → X ) from purely observational data. We begin by outlining a few basic definitions in the context of causal discovery, following the widely used do-Calculus [Pea00]. We continue by briefly reviewing a number of state-of-the-art methods, including very recent ones such as CGNN [Gou+17] and KCDC [MST18]. The main contribution is the introduction of a novel inference model where we assume a Bayesian hierarchical model, pursuing the strategy of Bayesian model selection. In our model the distribution of the cause variable is given by a Poisson lognormal distribution, which allows to explicitly regard discretization effects. We assume Fourier diagonal covariance operators, where the values on the diagonal are given by power spectra. In the most shallow model these power spectra and the noise variance are fixed hyperparameters. In a deeper inference model we replace the noise variance as a given prior by expanding the inference over the noise variance itself, assuming only a smooth spatial structure of the noise variance. Finally, we make a similar expansion for the power spectra, replacing fixed power spectra as hyperparameters by an inference over those, where again smoothness enforcing priors are assumed.
    [Show full text]
  • Articles Causal Inference in Civil Rights Litigation
    VOLUME 122 DECEMBER 2008 NUMBER 2 © 2008 by The Harvard Law Review Association ARTICLES CAUSAL INFERENCE IN CIVIL RIGHTS LITIGATION D. James Greiner TABLE OF CONTENTS I. REGRESSION’S DIFFICULTIES AND THE ABSENCE OF A CAUSAL INFERENCE FRAMEWORK ...................................................540 A. What Is Regression? .........................................................................................................540 B. Problems with Regression: Bias, Ill-Posed Questions, and Specification Issues .....543 1. Bias of the Analyst......................................................................................................544 2. Ill-Posed Questions......................................................................................................544 3. Nature of the Model ...................................................................................................545 4. One Regression, or Two?............................................................................................555 C. The Fundamental Problem: Absence of a Causal Framework ....................................556 II. POTENTIAL OUTCOMES: DEFINING CAUSAL EFFECTS AND BEYOND .................557 A. Primitive Concepts: Treatment, Units, and the Fundamental Problem.....................558 B. Additional Units and the Non-Interference Assumption.............................................560 C. Donating Values: Filling in the Missing Counterfactuals ...........................................562 D. Randomization: Balance in Background Variables ......................................................563
    [Show full text]
  • The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns
    atmosphere Review The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns Susan Prichard 1,*, N. Sim Larkin 2, Roger Ottmar 2, Nancy H.F. French 3 , Kirk Baker 4, Tim Brown 5, Craig Clements 6 , Matt Dickinson 7, Andrew Hudak 8 , Adam Kochanski 9, Rod Linn 10, Yongqiang Liu 11, Brian Potter 2, William Mell 2 , Danielle Tanzer 3, Shawn Urbanski 12 and Adam Watts 5 1 University of Washington School of Environmental and Forest Sciences, Box 352100, Seattle, WA 98195-2100, USA 2 US Forest Service Pacific Northwest Research Station, Pacific Wildland Fire Sciences Laboratory, Suite 201, Seattle, WA 98103, USA; [email protected] (N.S.L.); [email protected] (R.O.); [email protected] (B.P.); [email protected] (W.M.) 3 Michigan Technological University, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA; [email protected] (N.H.F.F.); [email protected] (D.T.) 4 US Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC 27709, USA; [email protected] 5 Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA; [email protected] (T.B.); [email protected] (A.W.) 6 San José State University Department of Meteorology and Climate Science, One Washington Square, San Jose, CA 95192-0104, USA; [email protected] 7 US Forest Service Northern Research Station, 359 Main Rd., Delaware, OH 43015, USA; [email protected] 8 US Forest Service Rocky Mountain Research Station Moscow Forestry Sciences Laboratory, 1221 S Main St., Moscow, ID 83843, USA; [email protected] 9 Department of Atmospheric Sciences, University of Utah, 135 S 1460 East, Salt Lake City, UT 84112-0110, USA; [email protected] 10 Los Alamos National Laboratory, P.O.
    [Show full text]
  • Journals in Assessment, Evaluation, Measurement, Psychometrics and Statistics
    Leadership Awards Publications Resources Membership About Journals in Assessment, Evaluation, Measurement, Psychometrics and Statistics Compiled by (mailto:[email protected]) Lihshing Leigh Wang (mailto:[email protected]) , Chair of APA Division 5 Public and International Affairs Committee. Journal Association/ Scope and Aims (Adapted from journals' mission Impact Publisher statements) Factor* American Journal of Evaluation (http://www.sagepub.com/journalsProdDesc.nav? American Explores decisions and challenges related to prodId=Journal201729) Evaluation conceptualizing, designing and conducting 1.16 Association/Sage evaluations. Offers original articles about the methods, theory, ethics, politics, and practice of evaluation. Features broad, multidisciplinary perspectives on issues in evaluation relevant to education, public administration, behavioral sciences, human services, health sciences, sociology, criminology and other disciplines and professional practice fields. The American Statistician (http://amstat.tandfonline.com/loi/tas#.Vk-A2dKrRpg) American Publishes general-interest articles about current 0.98^ Statistical national and international statistical problems and Association programs, interesting and fun articles of a general nature about statistics and its applications, and the teaching of statistics. Applied Measurement in Education (http://www.tandf.co.uk/journals/titles/08957347.asp) Taylor & Francis Because interaction between the domains of 0.33 research and application is critical to the evaluation and improvement of
    [Show full text]
  • Research Designs for Program Evaluations
    03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 79 CHAPTER 3 RESEARCH DESIGNS FOR PROGRAM EVALUATIONS Introduction 81 What Is Research Design? 83 The Origins of Experimental Design 84 Why Pay Attention to Experimental Designs? 89 Using Experimental Designs to Evaluate Programs: The Elmira Nurse Home Visitation Program 91 The Elmira Nurse Home Visitation Program 92 Random Assignment Procedures 92 The Findings 93 Policy Implications of the Home Visitation Research Program 94 Establishing Validity in Research Designs 95 Defining and Working With the Four Kinds of Validity 96 Statistical Conclusions Validity 97 Working with Internal Validity 98 Threats to Internal Validity 98 Introducing Quasi-Experimental Designs: The Connecticut Crackdown on Speeding and the Neighborhood Watch Evaluation in York, Pennsylvania 100 79 03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 80 80–●–PROGRAM EVALUATION AND PERFORMANCE MEASUREMENT The Connecticut Crackdown on Speeding 101 The York Neighborhood Watch Program 104 Findings and Conclusions From the Neighborhood Watch Evaluation 105 Construct Validity 109 External Validity 112 Testing the Causal Linkages in Program Logic Models 114 Research Designs and Performance Measurement 118 Summary 120 Discussion Questions 122 References 125 03-McDavid-4724.qxd 5/26/2005 7:16 PM Page 81 Research Designs for Program Evaluations–●–81 INTRODUCTION Over the last 30 years, the field of evaluation has become increasingly diverse in terms of what is viewed as proper methods and practice. During the 1960s and into the 1970s most evaluators would have agreed that a good program evaluation should emulate social science research and, more specifically, methods should come as close to randomized experiments as possible.
    [Show full text]
  • An Outline of Critical Thinking
    AN OUTLINE OF CRITICAL THINKING LEVELS OF INQUIRY 1. Information: correct understanding of basic information. 2. Understanding basic ideas: correct understanding of the basic meaning of key ideas. 3. Probing: deeper analysis into ideas, bases, support, implications, looking for complexity. 4. Critiquing: wrestling with tensions, contradictions, suspect support, problematic implications. This leads to further probing and then further critique, & it involves a recognition of the limitations of your own view. 5. Assessment: final evaluation, acknowledging the relative strengths & limitations of all sides. 6. Constructive: an articulation of your own view, recognizing its limits and areas for further inquiry. EMPHASES Issues! Reading: Know the issues an author is responding to. Writing: Animate and organize your paper around issues. Complexity! Reading: assume that there is more to an idea than is immediately obvious; assume that a key term can be used in various ways and clarify the meaning used in the article; assume that there are different possible interpretations of a text, various implications of ideals, and divergent tendencies within a single tradition, etc. Writing: Examine ideas, values, and traditions in their complexity: multiple aspects of the ideas, different possible interpretations of a text, various implications of ideals, different meanings of terms, divergent tendencies within a single tradition, etc. Support! Reading: Highlight the kind and degree of support: evidence, argument, authority Writing: Support your views with evidence, argument, and/or authority Basis! (ideas, definitions, categories, and assumptions) Reading: Highlight the key ideas, terms, categories, and assumptions on which the author is basing his views. Writing: Be aware of the ideas that give rise to your interpretation; be conscious of the definitions you are using for key terms; recognize the categories you are applying; critically examine your own assumptions.
    [Show full text]
  • Causation and Causal Inference in Epidemiology
    PUBLIC HEALTH MAnERS Causation and Causal Inference in Epidemiology Kenneth J. Rothman. DrPH. Sander Greenland. MA, MS, DrPH. C Stat fixed. In other words, a cause of a disease Concepts of cause and causal inference are largely self-taught from early learn- ing experiences. A model of causation that describes causes in terms of suffi- event is an event, condition, or characteristic cient causes and their component causes illuminates important principles such that preceded the disease event and without as multicausality, the dependence of the strength of component causes on the which the disease event either would not prevalence of complementary component causes, and interaction between com- have occun-ed at all or would not have oc- ponent causes. curred until some later time. Under this defi- Philosophers agree that causal propositions cannot be proved, and find flaws or nition it may be that no specific event, condi- practical limitations in all philosophies of causal inference. Hence, the role of logic, tion, or characteristic is sufiicient by itself to belief, and observation in evaluating causal propositions is not settled. Causal produce disease. This is not a definition, then, inference in epidemiology is better viewed as an exercise in measurement of an of a complete causal mechanism, but only a effect rather than as a criterion-guided process for deciding whether an effect is pres- component of it. A "sufficient cause," which ent or not. (4m JPub//cHea/f/i. 2005;95:S144-S150. doi:10.2105/AJPH.2004.059204) means a complete causal mechanism, can be defined as a set of minimal conditions and What do we mean by causation? Even among eral.
    [Show full text]
  • Experiments & Observational Studies: Causal Inference in Statistics
    Experiments & Observational Studies: Causal Inference in Statistics Paul R. Rosenbaum Department of Statistics University of Pennsylvania Philadelphia, PA 19104-6340 1 My Concept of a ‘Tutorial’ In the computer era, we often receive compressed • files, .zip. Minimize redundancy, minimize storage, at the expense of intelligibility. Sometimes scientific journals seemed to have been • compressed. Tutorial goal is: ‘uncompress’. Make it possible to • read a current article or use current software without going back to dozens of earlier articles. 2 A Causal Question At age 45, Ms. Smith is diagnosed with stage II • breast cancer. Her oncologist discusses with her two possible treat- • ments: (i) lumpectomy alone, or (ii) lumpectomy plus irradiation. They decide on (ii). Ten years later, Ms. Smith is alive and the tumor • has not recurred. Her surgeon, Steve, and her radiologist, Rachael de- • bate. Rachael says: “The irradiation prevented the recur- • rence – without it, the tumor would have recurred.” Steve says: “You can’t know that. It’s a fantasy – • you’re making it up. We’ll never know.” 3 Many Causal Questions Steve and Rachael have this debate all the time. • About Ms. Jones, who had lumpectomy alone. About Ms. Davis, whose tumor recurred after a year. Whenever a patient treated with irradiation remains • disease free, Rachael says: “It was the irradiation.” Steve says: “You can’t know that. It’s a fantasy. We’ll never know.” Rachael says: “Let’s keep score, add ’em up.” Steve • says: “You don’t know what would have happened to Ms. Smith, or Ms. Jones, or Ms Davis – you just made it all up, it’s all fantasy.
    [Show full text]
  • Project Quality Evaluation – an Essential Component of Project Success
    Mathematics and Computers in Biology, Business and Acoustics Project Quality Evaluation – An Essential Component of Project Success DUICU SIMONA1, DUMITRASCU ADELA-ELIZA2, LEPADATESCU BADEA2 Descriptive Geometry and Technical Drawing Department1, Manufacturing Engineering Department2, Faculty of Manufacturing Engineering and Industrial Management “Transilvania” University of Braşov 500036 Eroilor Street, No. 29, Brasov ROMANIA [email protected]; [email protected]; [email protected]; Abstract: - In this article are presented aspects, elements and indices regarding project quality management as a potential source of sustainable competitive advantages. The success of a project depends on implementation of the project quality processes: quality planning, quality assurance, quality control and continuous process improvement drive total quality management success. Key-Words: - project management, project quality management, project quality improvement. 1 Introduction negative impact on the possibilities of planning and Project management is the art — because it requires the carrying out project activities); skills, tact and finesse to manage people, and science — • supply chain problems; because it demands an indepth knowledge of an • lack of resources (funds or trained personnel); assortment of technical tools, of managing relatively • organizational inefficiency. short-term efforts, having finite beginning and ending - external factors: points, usually with a specific budget, and with customer • natural factors (natural disasters); specified performance criteria. “Short term” in the • external economic influences (adverse change in context of project duration is dependent on the industry. currency exchange rate used in the project); The longer and more complex a project is, the more it • reaction of people affected by the project; will benefit from the application of project management • implementation of economic or social policy tools.
    [Show full text]