Results 1 
6 of
6
The Mediation Formula: A guide to the assessment of causal pathways in nonlinear models
 STATISTICAL CAUSALITY. FORTHCOMING.
, 2011
"... ..."
Trygve Haavelmo and the Emergence of Causal Calculus
, 2012
"... 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. Th ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
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
Some Thoughts Concerning Transfer Learning, with Applications to Metaanalysis and Datasharing Estimation
, 2012
"... A deeply entrenched axiom in the theory of learning states that the more one learns the easier it is to learn. In other words, the more proficient one becomes in performing familiar tasks, the easier it is to learn new tasks. This phenomenon, long recognized by psychologists and educators, has also ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
A deeply entrenched axiom in the theory of learning states that the more one learns the easier it is to learn. In other words, the more proficient one becomes in performing familiar tasks, the easier it is to learn new tasks. This phenomenon, long recognized by psychologists and educators, has also been demonstrated in machine learning, especially in selftaught
Comment on “Causal inference, probability theory,
, 2013
"... Modern causal inference owes much of its progress to a strict and crisp distinction between probabilistic and causal information. This distinction recognizes that probability theory is insufficient for posing causal questions, let alone answering them, and dictates that every exercise in causal infe ..."
Abstract
 Add to MetaCart
Modern causal inference owes much of its progress to a strict and crisp distinction between probabilistic and causal information. This distinction recognizes that probability theory is insufficient for posing causal questions, let alone answering them, and dictates that every exercise in causal inference must commence with some extra knowledge that cannot be expressed in probability alone. 1 The paper by Baker attempts to overturn this distinction and argues that “probability theory is a desirable and sufficient basis for many topics in causal inference. ” My comments will disprove Baker’s claim, in the hope of convincing readers of the importance of keeping the boundaries between probabilistic and causal concepts crisp and visible. Baker’s argument begins with: “...besides explaining such causal graph topics as Mbias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical control ” (abstract). While I am not versed in the intricacies of “paired availability design ” (Google Scholar lists only a handful of entries in this category), I doubt it can be based solely on probabilities. Indeed, Baker himself resorts to counterfactuals and other nonprobabilistic notions2 in explaining the research questions a “paired availability design ” attempts to answer. I am quite familiar however with the concepts of “Mbias, ” “bias, ” “Simpson’s paradox, ” and “instrumental variable ” which I will show to have no interpretation in probability theory alone. I will start with the concept of “instrumental variable ” which should be familiar to most readers, and which is often mistaken to have probabilistic definition (see [2, pp. 387–389]). Assume we have a joint distribution P (x, y, z) defined on three variables X, Y, and Z. We ask: What condition should P satisfy in order for Z to qualify as an instrumental variable relative to the pair (X, Y). It is well known that, if X is 1 Cartwright [1] summarized this limitation in a wellknown slogan: “no causes in, no causes out.”
External Validity and Transportability: A Formal Approach
, 2011
"... We provide a formal definition of the notion of “transportability, ” or “external validity, ” as a license to transfer causal information from experimental studies to a different population in which only observational studies can be conducted. We introduce a formal representation called “selection d ..."
Abstract
 Add to MetaCart
We provide a formal definition of the notion of “transportability, ” or “external validity, ” as a license to transfer causal information from experimental studies to a different population in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams ” for expressing differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target population can be inferred from experimental findings in a different population. When the answer is affirmative, the procedures identify the set of experimental and observational studies that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in nonexperimental learning to minimize remeasurement cost and improve prediction power.