• Corpus ID: 211010812

Hypothesis Testing of Blip Effects in Sequential Causal Inference

@article{Wang2020HypothesisTO,
  title={Hypothesis Testing of Blip Effects in Sequential Causal Inference},
  author={Xiaoqin Wang and Li Yin},
  journal={arXiv: Methodology},
  year={2020}
}
In this article, we study the hypothesis testing of the blip / net effects of treatments in a treatment sequence. We illustrate that the likelihood ratio test and the score test may suffer from the curse of dimensionality, the null paradox and the high-dimensional constraint on standard parameters under the null hypothesis. On the other hand, we construct the Wald test via a small number of point effects of treatments in single-point causal inference. We show that the Wald test can avoid these… 

References

SHOWING 1-10 OF 12 REFERENCES
Structural Nested Mean Models for Assessing Time‐Varying Effect Moderation
TLDR
The results of a small simulation study begin to shed light on the small versus large sample performance of the estimators, and on the bias-variance trade-off between the two estimators are presented.
The central role of the propensity score in observational studies for causal effects
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.
Causal Inference from Complex Longitudinal Data
TLDR
Since the computer algorithms are well-defined mathematical objects, it is important to provide formal mathematical definitions for the English sentences expressing the investigator’s causal inferences.
Model assessment in dynamic treatment regimen estimation via double robustness
TLDR
This work demonstrates how the property of double robustness itself can be used to provide evidence that a specified model is or is not correct when analyzing data from observational studies using semi-parametric approaches.
Identifying and estimating net effects of treatments in sequential causal inference
Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post
Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.
TLDR
This work describes the parametric g-formula, and uses it to estimate the effect of various hypothetical lifestyle interventions on the risk of CHD using data from the Nurses' Health Study, the first large-scale application of the paramometric g- formula in an epidemiologic cohort study.
The Landscape of Causal Inference: Perspective From Citation Network Analysis
TLDR
This article gathers comprehensive information on publications and citations in causal inference and provides a review of the field from the perspective of citation network analysis, and examines the citation network through exponential random graph models (ERGMs).
Applied Regression Analysis
Basic Prerequisite Knowledge. Fitting a Straight Line by Least Squares. Checking the Straight Line Fit. Fitting Straight Lines: Special Topics. Regression in Matrix Terms: Straight Line Case. The
Association, Causation, And Marginal Structural Models
  • J. Robins
  • Philosophy, Computer Science
    Synthese
  • 2004
Dans le cadre de sa propre theorie formelle de l'inference causale contrefactuelle, l'A. presente quelques methodes et outils statistiques qui permettent de mesurer l'evidence des effets causaux dans
Causal Inference. CRC press, Boca Raton
  • 2018
...
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2
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