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Survival Analysis meets Counterfactual Inference [article]

Paidamoyo Chapfuwa, Serge Assaad, Shuxi Zeng, Michael Pencina, Lawrence Carin, Ricardo Henao
2020 arXiv   pre-print
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.  ...  There is growing interest in applying machine learning methods for counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited.  ...  Conclusions We have proposed the first unified counterfactual inference framework for survival analysis.  ... 
arXiv:2006.07756v1 fatcat:c4mm5k2bvnbbxdtjlukf4wvhye

Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research

Lauren Lapointe-Shaw, Zachary Bouck, Nicholas A. Howell, Theis Lange, Ani Orchanian-Cheff, Peter C. Austin, Noah M. Ivers, Donald A. Redelmeier, Chaim M. Bell
2018 BMC Medical Research Methodology  
Conclusion: There is increasing use of mediation analysis with time-to-event outcomes.  ...  Mediation analysis tests whether the relationship between two variables is explained by a third intermediate variable.  ...  The counterfactual or potential outcomes approach evolved more recently from the literature on causal inference [30] .  ... 
doi:10.1186/s12874-018-0578-7 fatcat:tvcv4jithfhkppszuczsjsxp24

A definition of causal effect for epidemiological research

M A Hernan
2004 Journal of Epidemiology and Community Health  
inference.  ...  The transplant did not have a causal effect on Hera's five day survival.  ... 
doi:10.1136/jech.2002.006361 pmid:15026432 pmcid:PMC1732737 fatcat:x6nmvlfwr5fbfkqrypcp2pebla

Towards Causal Modeling of Human Behavior [chapter]

Matteo Campo, Anna Polychroniou, Hugues Salamin, Maurizio Filippone, Alessandro Vinciarelli
2013 Smart Innovation, Systems and Technologies  
This article proposes experiments on decision making based on the "Winter Survival Task", one of the scenarios most commonly applied in behavioral and psychological studies.  ...  The goal of the Task is to identify, out of a predefined list of 12 items, those that are most likely to increase the chances of survival after the crash of a plane in a polar area.  ...  Conclusions This paper has presented a causal analysis of the decision-making behavior of individuals involved in the "Winter Survival Task".  ... 
doi:10.1007/978-3-642-35467-0_33 fatcat:zzb4d25htjggjitbl6pbkj5ima

Causality Learning: A New Perspective for Interpretable Machine Learning [article]

Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang
2021 arXiv   pre-print
This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning.  ...  Causal inference and causal discovery are two main research topics for causality analysis.  ...  Tools for Causal Analysis Several libraries or tools are available for causal inference.  ... 
arXiv:2006.16789v2 fatcat:ole3dvpnjnfkflldd6to4nrrwq

Assessing contribution of treatment phases through tipping point analyses using rank preserving structural failure time models [article]

Sudipta Bhattacharya, Jyotirmoy Dey
2020 arXiv   pre-print
Rank-preserving-structural-failure-time modeling is an approach for causal inference that is typically used to adjust for treatment switching in clinical trials with time to event endpoints.  ...  A tipping-point analysis is commonly used in situations where it is suspected that a statistically significant difference between treatment arms could be a result of missing or unobserved data instead  ...  BROCADE-3 Study Design Figure 3 3 Figure 3 Kaplan-Meier survival probability plot for progression free survival from analysis of a time-to-event endpoint (TTE).  ... 
arXiv:2011.09070v1 fatcat:jubayoy7izcbtkppm7anpnbx7u

Counterfactual Explanation of Machine Learning Survival Models

Maxim Kovalev, Lev Utkin, Frank Coolen, Andrei Konstantinov
2021 Informatica  
A method for counterfactual explanation of machine learning survival models is proposed.  ...  One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival  ...  Application of the PSO to the Survival Counterfactual Explanation Let us return to the counterfactual explanation problem in the framework of survival analysis.  ... 
doi:10.15388/21-infor468 fatcat:b4ibqxz4ufeq3byubl3w5hqin4

When Can History Be Our Guide? The Pitfalls of Counterfactual Inference

GARY KING, LANGCHE ZENG
2007 International Studies Quarterly  
Inferences about counterfactuals are essential for prediction, answering "what if " questions, and estimating causal effects.  ...  If an analysis fails the tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence.  ...  In the earlier section, we showed how all the counterfactuals in these data were extrapolations far from the convex hull and how, as a result, inferences about them were highly model-dependent.  ... 
doi:10.1111/j.1468-2478.2007.00445.x fatcat:gdvcefnpevenjaqvw2actmhuq4

Contextualising Causation Part I

Julian Reiss
2013 Philosophy Compass  
This is the first instalment of a two-part paper on the counterfactual theory of causation. It is well known that this theory is ridden with counterexamples.  ...  Specifically, the following four features of the theory suffer from problems: • it understands causation as a relation between events; • counterfactual dependence is understood using a metric of similarity  ...  Introduction It is well known that the counterfactual analysis of causation is flawed. 40 years after David Lewis lamented 'It remains to be seen whether any regularity analysis can succeed… without piling  ... 
doi:10.1111/phc3.12074 fatcat:bmupcyuvdnfftd5sk7leoihz6u

Structural accelerated failure time models for survival analysis in studies with time-varying treatments

Miguel A. Hernán, Stephen R. Cole, Joseph Margolick, Mardge Cohen, James M. Robins
2005 Pharmacoepidemiology and Drug Safety  
In contrast, a previous analysis using a standard (nonstructural) model did not find an effect of treatment on survival.  ...  Conclusions Our finding of a strongly beneficial effect is consistent with results from randomized trials and from a previous analysis of the same data using a marginal structural Cox model.  ...  Let T a be the counterfactual survival time and T a ½t the counterfactual hazard at time t under treatment value a (i.e. either 0 or 1).  ... 
doi:10.1002/pds.1064 pmid:15660442 fatcat:qel2oe73azg4baprlnyw5e4tum

Calculating control variables with age at onset data to adjust for conditions prior to exposure

Michael Höfler, Tanja Brueck, Roselind Lieb, Hans-Ulrich Wittchen
2005 Social Psychiatry and Psychiatric Epidemiology  
This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect.  ...  assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference  ...  Thus, all individuals meeting the criteria for Y already at baseline assessment are omitted from the analysis.  ... 
doi:10.1007/s00127-005-0944-8 pmid:16142510 fatcat:lcqnzg7ukzf53fjcccvmiksmdq

Confounding and Collapsibility in Causal Inference

Judea Pearl, James M. Robins, Sander Greenland
1999 Statistical Science  
It is our view that this property of counterfactual inferences reflects a strength of counterfactual approach, rather than a weakness.  ...  More constructively, the counterfactual approach also aids in precise formulation of assumptions needed to identify causal effects statistically, which in turn can aid in developing techniques for meeting  ... 
doi:10.1214/ss/1009211805 fatcat:5fyvemdx6ndotox3pyzj4ptt5i

Moving Forward: Developing Theoretical Contributions in Management Studies

Joep P. Cornelissen, Rodolphe Durand
2014 Journal of Management Studies  
This typology consists of various types of analogical and counterfactual reasoning, ranging from focused thought experiments aimed at prodding existing theory in the direction of alternative assumptions  ...  We thank the participants from those meetings for their feedback.  ...  The basic counterfactual inference forms the core of the theory Constitutive counterfactual: based on the assumption that an organization is an open (rather than closed) system, the inference that the  ... 
doi:10.1111/joms.12078 fatcat:en7t63ogj5hgfeweikcqljas7q

Page 29 of Biometrics Vol. 58, Issue 1 [page]

2002 Biometrics  
Inferential tools for sensitivity analysis and noncompliance in clinical trials. Proceedings of the Annual Meeting. Indianapolis, Indiana: American Statistical .Association. Goldberger, A. S. (1972).  ...  Comment on "Causal inference without counterfactuals,” by .A. P. Dawid. Journal of the American Statistical Association 95, 435 437. Scharfstein, D. O., Rotnitzky, A., and Robins, .1. M. (1999).  ... 

Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit

Jason H Maley, Kerollos N Wanis, Jessica G Young, Leo A Celi
2020 BMJ Health & Care Informatics  
They are not simply concerned about predicting if survival is possible.  ...  The outcome under our 'what if' scenario is contrary to what actually happened, that is, the counterfactual outcome.  ... 
doi:10.1136/bmjhci-2020-100220 pmid:33106330 pmcid:PMC7592248 fatcat:nwinxcn3mzhglekybomz6gbt4u
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