Richard Scheines
Biographic Data
| ID | 307488 |
|---|---|
| NAME | Richard Scheines |
| GIVEN NAMES | Richard |
| FAMILY NAME | Scheines |
| SIGNATURE | SCHEINES R |
| AFFILIATIONS | Carnegie Mellon University |
| VERIFIED | No |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 71 |
| WORKS AS AUTHOR | 16 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1986 |
| LATEST PUBLICATION YEAR | 2013 |
| H-INDEX | 5 |
Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling
In this study, the authors consider several indices to indicate whether multidimensional data are “unidimensional enough” to fit with a unidimensional measurement model, especially when the goal is to avoid excessive bias in structural parameter estimates. They examine two factor strength indices (the explained common variance and omega hierarchical) and several model fit indices (root mean square error of approximation, comparative fit index, an…
Actual causation
Interventions and Causal Inference
The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard’ and ‘soft’ interventions and discuss what they can contribute to causal discovery. We also describe how the choice…
Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study
Using data from a sample of 178 single black mothers and their young children who were ages three to five at time 1 and ages five to eight at time 2, this study examined the links between and among low-wage employment, mothers' self-efficacy beliefs, depressive symptoms, and a constellation of parenting behaviors in the preschool years to children's cognitive and behavioral functioning in early elementary school years. In general, the results sup…
The Similarity of Causal Inference in Experimental and Non-experimental Studies
For nearly as long as the word ‘correlation’ has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect the…
Causal Inference of Ambiguous Manipulations
Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, al…
Causation, Prediction, and Search
The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment. What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Cl…
The Tetrad Project
The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model's parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model's specification. We begin by drawing the analogy between parameter estimation and model specificati…
Using Path Diagrams as a Structural Equation Modeling Tool
A linear structural equation model (SEM) without free parameters has two parts: a probability distribution and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. This article shows how path diagrams can be used to solve a number of important problems in structural equation modeling; for example, How much do sample data underdetermine th…
Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search
Causation, Prediction, and Search
Reply to Comments
Simulation Studies of the Reliability of Computer-Aided Model Specification Using the Tetrad II, EQS, and Lisrel Programs
TETRAD II, a fully automated successor to the TETRAD program, is intended to aid in the respecification of underspecified linear causal models, or structural equation models. The performance of TETRAD II is compared with the automatic respecification procedures in the EQS and LISREL VI programs using 360 simulated data sets from nine different linear models containing 'latent' or unmeasured variables. LISREL VI and EQS each output a single sugges…
Tetrad
Exploring Causal Structure with the Tetrad Program
Department of Philosophy technical report
Causal modeling with the Tetrad program
Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study
Using data from a sample of 178 single black mothers and their young children who were ages three to five at time 1 and ages five to eight at time 2, this study examined the links between and among low-wage employment, mothers' self-efficacy beliefs, depressive symptoms, and a constellation of parenting behaviors in the preschool years to children's cognitive and behavioral functioning in early elementary school years. In general, the results sup…
Interventions and Causal Inference
The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard’ and ‘soft’ interventions and discuss what they can contribute to causal discovery. We also describe how the choice…
Causal Inference of Ambiguous Manipulations
Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, al…
The Similarity of Causal Inference in Experimental and Non-experimental Studies
For nearly as long as the word ‘correlation’ has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect the…
Actual causation
Using Path Diagrams as a Structural Equation Modeling Tool
A linear structural equation model (SEM) without free parameters has two parts: a probability distribution and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. This article shows how path diagrams can be used to solve a number of important problems in structural equation modeling; for example, How much do sample data underdetermine th…
Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search
Causal modeling with the Tetrad program
Tetrad
Exploring Causal Structure with the Tetrad Program
Department of Philosophy technical report
Reply to Comments
Simulation Studies of the Reliability of Computer-Aided Model Specification Using the Tetrad II, EQS, and Lisrel Programs
TETRAD II, a fully automated successor to the TETRAD program, is intended to aid in the respecification of underspecified linear causal models, or structural equation models. The performance of TETRAD II is compared with the automatic respecification procedures in the EQS and LISREL VI programs using 360 simulated data sets from nine different linear models containing 'latent' or unmeasured variables. LISREL VI and EQS each output a single sugges…
Causation, Prediction, and Search
Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search
The Tetrad Project
The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model's parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model's specification. We begin by drawing the analogy between parameter estimation and model specificati…
Using Path Diagrams as a Structural Equation Modeling Tool
A linear structural equation model (SEM) without free parameters has two parts: a probability distribution and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. This article shows how path diagrams can be used to solve a number of important problems in structural equation modeling; for example, How much do sample data underdetermine th…
Causation, Prediction, and Search
The authors address the assumptions and methods that allow us to turn observations into causal knowledge, and use even incomplete causal knowledge in planning and prediction to influence and control our environment. What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Cl…
Causal Inference of Ambiguous Manipulations
Over the last two decades, a fundamental outline of a theory of causal inference has emerged. However, this theory does not consider the following problem. Sometimes two or more measured variables are deterministic functions of one another, not deliberately, but because of redundant measurements. In these cases, manipulation of an observed defined variable may actually be an ambiguous description of a manipulation of some underlying variables, al…
Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study
Using data from a sample of 178 single black mothers and their young children who were ages three to five at time 1 and ages five to eight at time 2, this study examined the links between and among low-wage employment, mothers' self-efficacy beliefs, depressive symptoms, and a constellation of parenting behaviors in the preschool years to children's cognitive and behavioral functioning in early elementary school years. In general, the results sup…
The Similarity of Causal Inference in Experimental and Non-experimental Studies
For nearly as long as the word ‘correlation’ has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect the…
Interventions and Causal Inference
The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard’ and ‘soft’ interventions and discuss what they can contribute to causal discovery. We also describe how the choice…
Actual causation
Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling
In this study, the authors consider several indices to indicate whether multidimensional data are “unidimensional enough” to fit with a unidimensional measurement model, especially when the goal is to avoid excessive bias in structural parameter estimates. They examine two factor strength indices (the explained common variance and omega hierarchical) and several model fit indices (root mean square error of approximation, comparative fit index, an…
Computer Science (12 works) · Mathematics (10 works) · Bayesian Modeling and Causal Inference (8 works) · Epistemology (8 works) · Philosophy (8 works) · Statistics (7 works) · Psychology (6 works) · Causation (5 works) · Econometrics (5 works) · Tetrad (5 works)