scholarly journals Decision-Theoretic Foundations for Causal Reasoning

1995 ◽  
Vol 3 ◽  
pp. 405-430 ◽  
Author(s):  
D. Heckerman ◽  
R. Shachter
Keyword(s):  
Canonical Form ◽  
Special Class ◽  
Causal Reasoning ◽  
Directed Acyclic Graphs ◽  
Influence Diagrams ◽  
Counterfactual Reasoning ◽  
Cause And Effect ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Theoretic Foundations

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

scholarly journals An Introduction to Causal Inference

2020 ◽  
Author(s):  
Fabian Dablander
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Directed Acyclic Graphs ◽  
Causal Models ◽  
Causal Relationships ◽  
Counterfactual Reasoning ◽  
Graphical Approach ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Simpson's Paradox

Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. In this paper, I provide a concise introduction to the graphical approach to causal inference, which uses Directed Acyclic Graphs (DAGs) to visualize, and Structural Causal Models (SCMs) to relate probabilistic and causal relationships. Successively, we climb what Judea Pearl calls the "causal hierarchy" --- moving from association to intervention to counterfactuals. I explain how DAGs can help us reason about associations between variables as well as interventions; how the do-calculus leads to a satisfactory definition of confounding, thereby clarifying, among other things, Simpson's paradox; and how SCMs enable us to reason about what could have been. Lastly, I discuss a number of challenges in applying causal inference in practice.

scholarly journals CAUSAL ANALYSIS AFTER HAAVELMO

2014 ◽  
Vol 31 (1) ◽  
pp. 115-151 ◽  
Author(s):  
James Heckman ◽  
Rodrigo Pinto
Keyword(s):  
Economic Models ◽  
Causal Analysis ◽  
Directed Acyclic Graphs ◽  
Ceteris Paribus ◽  
Acyclic Graphs ◽  
Rigorous Treatment ◽  
Definition Of

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined usinghypotheticalmodels that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890)ceteris paribusanalysis. We embed Haavelmo’s framework into the recursive framework of Directed Acyclic Graphs (DAGs) commonly used in the literature of causality (Pearl, 2000) and Bayesian nets (Lauritzen, 1996). We compare the analysis of causality based on a methodology inspired by Haavelmo’s ideas with other approaches used in the causal literature of DAGs. We discuss the limitations of methods that solely use the information expressed in DAGs for the identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo.

Modeling overlapping structures

2013 ◽  
Author(s):  
Yves Marcoux ◽  
Michael Sperberg-McQueen ◽  
Claus Huitfeldt
Keyword(s):  
Directed Graphs ◽  
Upper Bounds ◽  
Directed Acyclic Graphs ◽  
Markup Language ◽  
Multiple Roots ◽  
Acyclic Graph ◽  
Xml Documents ◽  
Acyclic Graphs ◽  
Structured Document ◽  
Definition Of

The problem of overlapping structures has long been familiar to the structured document community. In a poem, for example, the verse and line structures overlap, and having them both available simultaneously is convenient, and sometimes necessary (for example for automatic analyses). However, only structures that embed nicely can be represented directly in XML. Proposals to address this problem include XML solutions (based essentially on a layer of semantics) and non-XML ones. Among the latter is TexMecs HS2003, a markup language that allows overlap (and many other features). XML documents, when viewed as graphs, correspond to trees. Marcoux M2008 characterized overlap-only TexMecs documents by showing that they correspond exactly to completion-acyclic node-ordered directed acyclic graphs. In this paper, we elaborate on that result in two ways. First, we cast it in the setting of a strictly larger class of graphs, child-arc-ordered directed graphs, that includes multi-graphs and non-acyclic graphs, and show that — somewhat surprisingly — it does not hold in general for graphs with multiple roots. Second, we formulate a stronger condition, full-completion-acyclicity, that guarantees correspondence with an overlap-only document, even for graphs that have multiple roots. The definition of fully-completion-acyclic graph does not in itself suggest an efficient algorithm for checking the condition, nor for computing a corresponding overlap-only document when the condition is satisfied. We present basic polynomial-time upper bounds on the complexity of accomplishing those tasks.

scholarly journals Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emilia Gvozdenović ◽  
Lucio Malvisi ◽  
Elisa Cinconze ◽  
Stijn Vansteelandt ◽  
Phoebe Nakanwagi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Causal Inference ◽  
Observational Studies ◽  
Vaccine Development ◽  
Directed Acyclic Graphs ◽  
Counterfactual Reasoning ◽  
Confounding Bias ◽  
Accurate Design ◽  
Acyclic Graphs ◽  
Causal Diagrams

Abstract Background Randomized controlled trials are considered the gold standard to evaluate causal associations, whereas assessing causality in observational studies is challenging. Methods We applied Hill’s Criteria, counterfactual reasoning, and causal diagrams to evaluate a potentially causal relationship between an exposure and outcome in three published observational studies: a) one burden of disease cohort study to determine the association between type 2 diabetes and herpes zoster, b) one post-authorization safety cohort study to assess the effect of AS04-HPV-16/18 vaccine on the risk of autoimmune diseases, and c) one matched case-control study to evaluate the effectiveness of a rotavirus vaccine in preventing hospitalization for rotavirus gastroenteritis. Results Among the 9 Hill’s criteria, 8 (Strength, Consistency, Specificity, Temporality, Plausibility, Coherence, Analogy, Experiment) were considered as met for study c, 3 (Temporality, Plausibility, Coherence) for study a, and 2 (Temporary, Plausibility) for study b. For counterfactual reasoning criteria, exchangeability, the most critical assumption, could not be tested. Using these tools, we concluded that causality was very unlikely in study b, unlikely in study a, and very likely in study c. Directed acyclic graphs provided complementary visual structures that identified confounding bias and helped determine the most accurate design and analysis to assess causality. Conclusions Based on our assessment we found causal Hill’s criteria and counterfactual thinking valuable in determining some level of certainty about causality in observational studies. Application of causal inference frameworks should be considered in designing and interpreting observational studies.

The Lorax Problem: Introduction to Bayesian Networks

2019 ◽  
pp. 325-352
Author(s):  
Therese M. Donovan ◽  
Ruth M. Mickey
Keyword(s):  
Bayesian Networks ◽  
Joint Probability ◽  
Bayes Theorem ◽  
Directed Acyclic Graphs ◽  
Causal Models ◽  
Influence Diagrams ◽  
Acyclic Graphs ◽  
New Information ◽  
Alternative Hypotheses ◽  
The Lorax

The “Lorax Problem” introduces Bayesian networks, another set of methods that makes use of Bayes’ Theorem. The ideas are first explained in terms of a small, standard example that explores two alternative hypotheses for why the grass is wet: the sprinkler is on versus it is raining. The chapter describes how to depict causal models graphically with the use of influence diagrams and directed acyclic graphs. Bayes’ Theorem is used to compute conditional probabilities and to update probabilities once new information is obtained or assumed. The software program Netica is introduced. Finally, the chapter provides a second example of Bayesian networks based on The Lorax by Dr. Seuss. The reader will gain a firm understanding of parent nodes (also known as root nodes), child nodes, conditional probability tables (CPTs), and the chain rule for joint probability.

scholarly journals "Toward a Clearer Definition of Confounding" Revisited With Directed Acyclic Graphs

2012 ◽  
Vol 176 (6) ◽  
pp. 506-511 ◽  
Author(s):  
P. P. Howards ◽  
E. F. Schisterman ◽  
C. Poole ◽  
J. S. Kaufman ◽  
C. R. Weinberg
Keyword(s):  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Definition Of

scholarly journals Oropharyngeal Dysphagia: A Proposal for an Ecological Theoretical Model

2022 ◽  
pp. 7-16
Author(s):  
Rafaela Soares Rech ◽  
Bárbara Niegia Garcia de Goulart
Keyword(s):  
Data Analysis ◽  
Theoretical Model ◽  
Oropharyngeal Dysphagia ◽  
Theoretical Models ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Direct Causation ◽  
Definition Of

Background: The exponential growth in epidemiological studies has been reflected in an increase in analytical studies. Thus, theoretical models are required to guide the definition of data analysis, although so far, they are seldom used in Speech, Language, and Hearing Sciences. Objective: To propose a multicausal model for oropharyngeal dysphagia using directed acyclic graphs showing mediating variables, confounding variables, and variables connected by direct causation. Design: This integrative literature review. Setting: This was carried out until January 4, 2021, and searches were performed with the MEDLINE, EMBASE,and other bases.

scholarly journals Decision-theoretic foundations for statistical causality

2021 ◽  
Vol 9 (1) ◽  
pp. 39-77
Author(s):  
Philip Dawid
Keyword(s):  
Decision Making ◽  
Causal Inference ◽  
Decision Problem ◽  
Directed Acyclic Graphs ◽  
Intention To Treat ◽  
External Data ◽  
Acyclic Graphs ◽  
Context Dependent ◽  
Theoretic Foundations

Abstract We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as “assisted decision-making” and aims to understand when, and how, I can make use of external data, typically observational, to help me solve a decision problem by taking advantage of assumed relationships between the data and my problem. The relationships embodied in any representation of a causal problem require deeper justification, which is necessarily context-dependent. Here we clarify the considerations needed to support applications of the DT methodology. Exchangeability considerations are used to structure the required relationships, and a distinction drawn between intention to treat and intervention to treat forms the basis for the enabling condition of “ignorability.” We also show how the DT perspective unifies and sheds light on other popular formalisations of statistical causality, including potential responses and directed acyclic graphs.

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