[PDF] Top 20 Causal Inference Beyond Estimating Average Treatment Effects
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Causal Inference Beyond Estimating Average Treatment Effects
... posite or “pseudo” likelihood rather than a true likelihood because the binomial random variables are actually dependent but are treated as independent in the composite likeli- hood. Composite likelihood has been found ... See full document
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Topics In Statistical Inference For Treatment Effects
... There are four sections in the display. The first section is a recall for the ivmodel expression and the sample size. The second section summarizes the first stage regression between the IV and exposure. Here the F ... See full document
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A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study
... weighted average of odds ratios from each observed pattern of con- ...when estimating odds ratios, condi- tional effects may differ from marginal effects even in the absence of confounding ... See full document
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Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?
... the treatment effect changes over time, or when long-term trends in hazards are not established in the short term, because longer-term information is ...the treatment effect size, but have only considered ... See full document
20
Agent-Based Models for Causal Inference
... In our simplified example, mortality risk was always correctly estimated when model parameters were estimated from the same population to which the model was applied, regardless of whether we used the parametric ... See full document
133
Essays on causal inference and political representation
... the treatment variable—the level of identification required—is ...the effects of voter identification require- ments on the likelihood that voters participated in these two presidential elections, a ... See full document
100
G-computation of average treatment effects on the treated and the untreated
... in estimating the im- pact of a hypothetical intervention (aimed at ensuring that the target study participants have at least a high school education) on angina ... See full document
5
Challenges of Using Text Classifiers for Causal Inference
... determining causal effects of treatments on outcomes, they can be expensive or impossible in many ...of causal inference examines what assumptions and analyses make it possible to identify ... See full document
13
Extended conditional independence and applications in causal inference
... the average effects of giving treatment versus placebo for a given ...the treatment choice and the variable of ...receiving treatment T = t, cannot necessarily be assumed to be the same ... See full document
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Doubly Robust Causal Inference With Complex Parameters
... that treatment effects do not vary across units, and also require correct parametric models for how both the treat- ment and outcome processes depend on covariates and instruments (Wooldridge, ... See full document
138
The effects of nutrients on stream invertebrates: a regional estimation by generalized propensity score
... for causal inference The fundamental concept in causal inference is the con- cept of counterfactual, which requires that the responses to treatment and control be measured from the same ... See full document
13
Flexible causal inference for political science
... in causal inference have been forced to choose which unpalatable assumptions they wished to embrace in the face of endogeneity issues: they could utilize a potentially weak or invalid instrument, assume ... See full document
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Flexible Causal Inference for Political Science
... in causal inference have been forced to choose which unpalatable assumptions they wished to embrace in the face of endogeneity issues: they could utilize a potentially weak or invalid instrument, assume ... See full document
43
Statistical Analysis in Empirical Bayes and in Causal inference
... both treatment arm and control arm, so it is natural to incorporate this constraint in the empirical ...of estimating equations and leads to poor performance of empirical likelihood as in the examples in ... See full document
134
Causal inference based on counterfactuals
... in treatment or because indi- viduals deposit drug intake because of adverse ...statistical inference for causal effects of time-dependent exposures is often based on sur- vival time as ... See full document
12
Estimating Causal Treatment Effects via the Propensity Score and Estimating Survival Distributions in Clinical Trials That Follow Two-Stage Randomization Designs
... where K b (t) = Q u≤t { 1 − dN c (u)/Y (u) } , is the Kaplan-Meier estimate of the censor- ing survivor curve, with N c (u) = P n i=1 I(V i ≤ u, ∆ i = 0) and Y (u) = P n i=1 I(V i ≥ u). The estimator F b 1k (t) thus ... See full document
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Accounting for Uncertainty in Confounder and Effect Modifier Selection When Estimating Average Causal Effects in Generalized Linear Models
... model-averaged causal effect estimation with propensity scores that share important similarities with ...to causal inference in high dimensional settings where ... See full document
22
Causal Inference Using Variation In Treatment Over Time
... the inference, the Type I error rate is valid even if parametric models for responses are misspecified such as failing to account for cluster-by-time ...randomization inference to stepped- wedge ... See full document
105
Bayesian Methods for Optimal Treatment Allocation and Causal Inference.
... frequentist inference in various applications ...for causal inference because many full sample estimators are available for estimating average causal effects (ACE) such as ... See full document
111
Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects
... optimizing causal analyses with nonran- domized ...for causal inference, while HDPS is optimized for health care databases with the majority of variables being binary or cat- ...on estimating ... See full document
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