Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection. Finally, we observe that modern economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, as a result, econometric research has not fully utilized modern advances in causal analysis. 1

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers ’ beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, d-separation, confounding, endogenous selection, and over control. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than an associational concept, that conventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, time-point treatments (including the famous backdoor criterion), as well identification criteria for multiple, time-varying treatments.

This report surveys six influential econometric textbooks in terms of their mathematical treatment of causal concepts. It highlights conceptual and notational differences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that econonometric textbooks vary from complete denial to partial acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent mathematical notation that distinguishes causal from statistical concepts. This survey also provides a panoramic view of the state of causal thinking in econometric education which, to the best of our knowledge, has not been surveyed before. 1

Abstract. The generalizability of empirical findings to new environ-ments, settings or populations, often called “external validity, ” is es-sential in most scientific explorations. This paper treats a particular problem of generalizability, called “transportability”, defined as a li-cense to transfer causal effects learned in experimental studies to a new population, in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams ” for ex-pressing knowledge about differences and commonalities between pop-ulations of interest and, using this representation, we reduce questions of transportability to symbolic derivations in the do-calculus. This re-duction yields graph-based procedures for deciding, prior to observing any data, whether causal effects in the target population can be in-ferred from experimental findings in the study population. When the answer is affirmative, the procedures identify what experimental and observational findings need be obtained from the two populations, and how they can be combined to ensure bias-free transport. Key words and phrases: experimental design, generalizability, causal effects, external validity.

This paper contributes to the literature by carrying out the first empirical investigation into the role of different types of enterprises in the creation of social trust. Drawing on a unique dataset collected through the administration of a questionnaire to a representative sample of the population of the Italian Province of Trento in March 2011, we find that cooperatives are the only type of enterprise where the work environment fosters the social trust of workers.

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Throughout the 20th century (and well before) causal infer-ence has been an active area of inquiry, with a new burst of activity accompanying the first part of the 21st century. An incomplete list of people who have made important contributions over the past half-century includes

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Abstract—Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a com-bination of statistical data and qualitative causal assumptions [1]– [3]. SEM allows for both confirmatory and exploratory modeling. In exploratory modeling one starts with the specification of a hypothesis, which is tested against measurements by measuring how well the model fits the data. In exploratory modeling one searches the model space without stating a prior hypothesis. Exploratory modeling has the benefit that no prior background knowledge is needed, but has the drawback that the model search space grows super-exponentially since for n variables the number of SEM models is n4n. In the present paper we use an evolutionary algorithm approach to deal with the large search space in order to obtain good solutions within a reasonable amount of computation time. In addition, instead of dealing with one objective, we deal with multiple objectives to obtain more robust specifications. For this we employ the multi-objective evolutionary algorithm (MOEA) approach by using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). At the end, to confirm the stability of a specification, we employ a stability selection approach. We validate our approach on a data set which is generated from an artificial model. Experimental results show that our procedure allows for stable inference of a causal model. Keywords—Stable specification search, Structural Equation Modeling, Multi-objective optimization.