Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “conditional effects”

Conditional separable effects

Stensrud, Mats J., Robins, James M., Sarvet, Aaron et al. · Figshare · 2022

Researchers are often interested in treatment effects on outcomes that are only defined conditional on post-treatment events. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, naive contrasts of outcomes conditional on post-treatment events are not average causal effects, even in randomized experiments. Therefore, the effect in the principal stratum of those who would have the same value of the post-treatment variable regardless of treatment (such as the survivor average causal effect) is often advocated for causal inference. While principal stratum effects are average causal effects, they refer to a subset of the population that cannot be observed and may not exist. Therefore, it is not clear how these effects inform decisions or policies. Here we propose the <i>conditional separable effects</i>, quantifying causal effects of modified versions of the study treatment in an observable subset of the population. These effects, which may quantify direct effects of the study treatment, require transparent reasoning about candidate modified treatments and their mechanisms. We provide identifying conditions and various estimators of these effects along with an applied example.

Cited by 0Open access

Conditional covariate effects.

Leon Di Stefano (13883550), Elizabeth L. Ogburn (4042610), Malathi Ram (347523) et al. · Figshare · 2022

&lt;p&gt;Conditional effects from the Bayesian proportional odds model. Shown are (1) the relative risk of mechanical ventilation or death at day 28–35; (2) the estimated probabilities of mechanical ventilation or death at day 28–35 under control and HCQ/CQ; and (3) the log proportional odds ratio comparing HCQ/CQ and control. Each of these effects are shown for reference individuals with the following covariate values: age 60, BMI 25, no baseline comorbidities, baseline ordinal score of 5, and sex coefficient set between male and female values. Curves for continuous covariates are accompanied by 50% and 95% credible bands; intervals for discrete covariates are accompanied by 66% and 95% credible intervals. BMI indicates body mass index; HCQ/CQ, hydroxychloroquine or chloroquine.&lt;/p&gt; &lt;p&gt;(PDF)&lt;/p&gt;

Cited by 0Open access

Conditional Separable Effects

Mats Julius Stensrud, James M. Robins, Aaron L. Sarvet et al. · Journal of the American Statistical Association · 2022

Researchers are often interested in treatment effects on outcomes that are only defined conditional on posttreatment events. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, naive contrasts of outcomes conditional on posttreatment events are not average causal effects, even in randomized experiments. Therefore, the effect in the principal stratum of those who would have the same value of the posttreatment variable regardless of treatment (such as the survivor average causal effect) is often advocated for causal inference. While principal stratum effects are average causal effects, they refer to a subset of the population that cannot be observed and may not exist. Therefore, it is not clear how these effects inform decisions or policies. Here we propose the <i>conditional separable effects</i>, quantifying causal effects of modified versions of the study treatment in an observable subset of the population. These effects, which may quantify direct effects of the study treatment, require transparent reasoning about candidate modified treatments and their mechanisms. We provide identifying conditions and various estimators of these effects along with an applied example. Supplementary materials for this article are available online.

Cited by 34Open access

Conditional direct effects.

Ioana Gutu (20114055), Camelia Nicoleta Medeleanu (20114058), Romeo Asiminei (20114061) · Figshare · 2024

&lt;div&gt;&lt;p&gt;There is convincing evidence that the learning environments digitalization of tools and equipment ultimately results in the speed and depth learning involvement of academia members, by raising attainment of each of the digital learning experiences. The majority of the research that was conducted on the topic of enhancing the digital skills of learners, which would ultimately lead to an increase in their active engagement, was conducted on students in primary and secondary education, leaving members of higher education outside of the scope of the study. Given the uninterrupted search for academic performance and innovation, the current research considers the technological changes that lead to the transformation of the traditional academic learning environments as previously known. The current paper considers the changes in the learners’ engagement in the context of the dually digital transformation of the higher academic multi-institutional digitally-learning enhancements. An important factor to be considered regards the leadership evolution (in terms of teaching) that over time, led to a different speed contextual shift, according to its effectiveness, leading to higher or lower students learning (dis)engagement. The current manuscript aims to examine how the higher education digitalization levels could affect the student’s learning engagement, under the close monitoring of the academia leadership styles practice. Data collection and analysis implied at first a qualitative approach by issuing an online-distributed survey that resulted in a number of 2272 valid responses. After performing structural equation modelling and proving a valid assessment tool, the analysis resulted into statistically proving the validity of two main hypotheses according to which students learning engagement has a positive effect on the practice of academic leadership. Additionally, results emphasized the fact that higher education digitalization has altogether a negative effect of students learning engagement. Consequently, the current study stresses on the importance of different peers’ categories in the context of higher education institutions performance, with an emphasis on the different levels of students’ engagement and the leadership styles evolution and practice, aspects uniformly developing within a continuously digitally transformation of the higher education environment.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Conditional Separable Effects

Stensrud, Mats J., Robins, James M., Sarvet, Aaron et al. · Figshare · 2022

Researchers are often interested in treatment effects on outcomes that are only defined conditional on posttreatment events. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, naive contrasts of outcomes conditional on posttreatment events are not average causal effects, even in randomized experiments. Therefore, the effect in the principal stratum of those who would have the same value of the posttreatment variable regardless of treatment (such as the survivor average causal effect) is often advocated for causal inference. While principal stratum effects are average causal effects, they refer to a subset of the population that cannot be observed and may not exist. Therefore, it is not clear how these effects inform decisions or policies. Here we propose the <i>conditional separable effects</i>, quantifying causal effects of modified versions of the study treatment in an observable subset of the population. These effects, which may quantify direct effects of the study treatment, require transparent reasoning about candidate modified treatments and their mechanisms. We provide identifying conditions and various estimators of these effects along with an applied example. Supplementary materials for this article are available online.

Cited by 0Open access

Identifying Conditional Causal Effects

Jin Tian · arXiv (Cornell University) · 2012

This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution.

Cited by 2Open access

Conditional analysis of effects.

Hung Manh Nguyen (8774633), Carmen Lindsay (16436838), Mohammad Baradaran (16436841) et al. · Figshare · 2023

&lt;div&gt;&lt;p&gt;Objective&lt;/p&gt;&lt;p&gt;An instrument for measuring intervention preferences applicable to both patients and policymakers would make it possible to better confront the needs of the supply and demand sides of the health care system. This study aimed to develop a discrete choice experiments (DCE) questionnaire to elicit the preferences of patients and policymakers. The instrument was specifically developed to estimate preferences for new conditions to be added to a screening program for fetal chromosomal anomalies.&lt;/p&gt;&lt;p&gt;Methods&lt;/p&gt;&lt;p&gt;A DCE development study was conducted. The methods employed included a literature review, a qualitative study (based on individual semi-structured interviews, consultations, and a focus group discussion) with pregnant women and policymakers, and a pilot project with 33 pregnant women to validate the first version of the instrument and test the feasibility of its administration.&lt;/p&gt;&lt;p&gt;Results&lt;/p&gt;&lt;p&gt;An initial list of 10 attributes was built based on a literature review and the qualitative research components of the study. Five attributes were built based on the responses provided by the participants from both groups. Eight attributes were consensually retained. A pilot project performed on 33 pregnant women led to a final instrument containing seven attributes: ‘conditions to be screened’, ‘test performance’, ‘moment at gestational age to obtain the test result’, ‘degree of test result certainty to the severity of the disability’, ‘test sufficiency’, ‘information provided from test result’, and ‘cost related to the test’.&lt;/p&gt;&lt;p&gt;Conclusion&lt;/p&gt;&lt;p&gt;It is possible to reach a consensus on the construction of a DCE instrument intended to be administered to pregnant women and policymakers. However, complete validation of the consensual instrument is limited because there are too few voting members of health technology assessment agencies committees to statistically ascertain the relevance of the attributes and their levels.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Conditional average treatment effects.

Rina Friedberg (6807497), Michael Baiocchi (838887), Evan Rosenman (6807500) et al. · Figshare · 2023

&lt;p&gt;CATE estimates, shown as a histogram (left) and varying with covariate values (right).&lt;/p&gt;

Cited by 0Open access

Conditional mixed-effects logit model.

Vanessa M. Adams (203400), Robert L. Pressey (203409), Natalie Stoeckl (571941) · Figshare · 2015

&lt;p&gt;CC indicates conservation covenant; CMA indicates conservation management agreement. Configuration was coded as a set of dummy variables (corresponding to alternative changes in configuration in &lt;a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097941#pone-0097941-g002" target="_blank"&gt;Figure 2&lt;/a&gt;) with configuration 1 chosen as the status quo.&lt;/p&gt;&lt;p&gt;*p&lt;0.05,&lt;/p&gt;&lt;p&gt;**p&lt;0.005,&lt;/p&gt;&lt;p&gt;***p&lt;0.001.&lt;/p&gt;

Cited by 0Open access

Conditional Effects, Observables and Instruments

Stan Gudder · Quanta · 2024

We begin with a study of operations and the effects they measure. We define the probability that an effect $a$ occurs when the system is in a state $\rho$ by $P_{\rho}(a)=\textrm{Tr}(\rho a)$. If $P_{\rho}(a)\ne0$ and $\mathcal{I}$ is an operation that measures $a$, we define the conditional probability of an effect $b$ given $a$ relative to $\mathcal{I}$ by $P_{\rho}(b\mid a)=\textrm{Tr}[\mathcal{I}(\rho)b]/P_{\rho}(a)$. We characterize when Bayes' quantum second rule $P_{\rho}(b\mid a)=\frac{P_{\rho}(b)}{P_{\rho}(a)}\,P_{\rho}(a\mid b)$ holds. We then consider Lüders and Holevo operations. We next discuss instruments and the observables they measure. If $A$ and $B$ are observables and an instrument $\mathcal{I}$ measures $A$, we define the observable $B$ conditioned on $A$ relative to $\mathcal{I}$ and denote it by $(B\mid A)$. Using these concepts, we introduce Bayes' quantum first rule. We observe that this is the same as the classical Bayes' first rule, except it depends on the instrument used to measure $A$. We then extend this to Bayes' quantum first rule for expectations. We show that two observables $B$ and $C$ are jointly commuting if and only if there exists an atomic observable $A$ such that $B=(B\mid A)$ and $C=(C\mid A)$. We next obtain a general uncertainty principle for conditioned observables. Finally, we discuss observable conditioned quantum entropies. The theory is illustrated with many examples. Quanta 2024; 13: 1–10.

Cited by 1Open access

Conditional effects and confidence bands.

Gregor Genrich (10213880), Céline Zeller (10213883), Hans Jörg Znoj (10213886) · Figshare · 2021

&lt;p&gt;Horizontal: z-values for protective behavioral strategies for marijuana (PBSM). Vertical: Effect of routine motives on the severity of dependence scale (SDS). The two grey dotted lines are confidence bands and represent the confidence interval for each point on the regression line. If they are not both either above or below zero, the conditional effect of the predictor is no longer significant. The black regression line represents how the use of PBSM affects the association between routine motives and SDS: It is weaker for higher values of PBSM-use. For value below 1.62, routine motives significantly predict SDS. For values above 1.62, this association is no longer significant.&lt;/p&gt;

Cited by 0Open access

EffectLiteR: Average and Conditional Effects

Axel Mayer · 2015

Use structural equation modeling to estimate average and conditional effects of a treatment variable on an outcome variable, taking into account multiple continuous and categorical covariates.Depends R (>= 3.5.0),lavaan (>= 0.6-8) Imports methods, shiny (>= 1.5.0),DT, foreign, ggplot2, nnet, car, restriktor, ic.infer, numDeriv Suggests

Cited by 2Open access