# Study Design in Causal Models

@article{Karvanen2015StudyDI, title={Study Design in Causal Models}, author={Juha Karvanen}, journal={Scandinavian Journal of Statistics}, year={2015}, volume={42}, pages={361 - 377} }

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing‐data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by…

## 24 Citations

Identifying Causal Effects with the R Package causaleffect

- Mathematics
- 2017

The R package causaleffect is presented, which provides an implementation of Shpitser and Pearl's (2006b) algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs.

Do-search -- a tool for causal inference and study design with multiple data sources

- Computer Science
- 2020

Do-search is presented, a recently developed algorithmic approach that can determine the identifiability of a causal effect and is based on do-calculus, and it can utilize data with non-trivial missing data and selection bias mechanisms.

Estimating complex causal effects from observational data

- Computer ScienceArXiv
- 2014

It is demonstrated that the estimation of causal effects does not necessarily require the causal model to be specified parametrically but it suffices t model directly the observational probability distributions.

Estimating complex causal effects from incomplete observational data

- Computer Science
- 2014

It is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known.

Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys

- Business
- 2016

It was found that multiple imputation removed almost all differences between full sample and estimated prevalences and the inverse probability weighting removed more than half and the doubly robust method 60% of the differences.

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach

- Computer ScienceJ. Stat. Softw.
- 2021

A search algorithm directly over the rules of do-calculus is presented, capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and experimental source distributions and of handling missing data problems as well.

Do-search

- Computer ScienceEpidemiology
- 2020

It is shown how causal effects can be identified and estimated by combining experiments and observations in real and realistic scenarios and presented as a new tool, do-search, a recently developed algorithmic approach that can determine the identifiability of a causal effect.

Statistical modelling of selective non-participation in health examination surveys

- Medicine
- 2018

The thesis demonstrates that the use of additional data sources and these statistical methods leads to prevalence estimates for daily smoking and heavy alcohol consumption that are higher than those obtained from the participants only.

Optimal design of observational studies: overview and synthesis

- Computer Science
- 2016

A unifying framework that allows us to use the same concepts and notation for different problems in the planning of observational studies is proposed and methods for deriving optimal or approximately optimal designs are discussed.

Simulation Framework for Realistic Large-scale Individual-level Health Data Generation

- Computer Science
- 2020

The main use cases of the framework are predicting the development of risk factors and disease occurrence, evaluating the impact of interventions and policy decisions, and statistical method development.

## References

SHOWING 1-10 OF 59 REFERENCES

For objective causal inference, design trumps analysis

- Economics
- 2008

For obtaining causal inferences that are objective, and therefore have the best chance of revealing scientific truths, carefully designed and executed randomized experiments are generally considered…

Causal diagrams for empirical research

- Philosophy
- 1995

SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper…

Identification of Causal Effects Using Instrumental Variables

- Economics
- 1993

It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.

A general identification condition for causal effects

- Economics, MathematicsAAAI/IAAI
- 2002

The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion on any set of variables.

A Bayesian Method for Causal Modeling and Discovery Under Selection

- Computer ScienceUAI
- 2000

A Bayesian method for combining data under selection with prior beliefs in order to derive a posterior probability for a model of the causal processes that are generating the data in the population of interest.

Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models

- MathematicsAAAI
- 2006

A necessary and sufficient graphical condition is provided for the cases when the causal effect of an arbitrary set of variables on another arbitrary set can be determined uniquely from the available information, as well as an algorithm which computes the effect whenever this condition holds.

Causality: Models, Reasoning and Inference

- Philosophy
- 2000

1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.…

Pearl's Calculus of Intervention Is Complete

- MathematicsUAI
- 2006

It is proved that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-Calculus that transforms the causal effect formula into a formula that only includes observational quantities.

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

- MedicinePreventive medicine
- 2007

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study, resulting in a checklist of 22 items that relate to the title, abstract, introduction, methods, results, and discussion sections of articles.

Causal Inference by Surrogate Experiments: z-Identifiability

- Mathematics, Computer ScienceUAI
- 2012

The problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation is addressed and a graphical necessary and sufficient condition for z-identifiability for arbitrary sets X,Z, and Y is provided.