How Much Should We Trust Estimates from Multiplicative Interaction

... groups and examine the range of X values for which there is a sufficient number of data points in both groups. In our example, we see that we see that both groups share a common support of X for the range between about 1.5 to 5 as we would expect given the simulation parameters.8 If the moderator is ...

... groups and examine the range of X values for which there is a sufficient number of data points in both groups. In our example, we see that we see that both groups share a common support of X for the range between about 1.5 to 5 as we would expect given the simulation parameters.8 If the moderator is ...

A framework for the investigation of pleiotropy in two

... An array of sophisticated techniques exist for estimating the causal effect with individual participant data. However, the sharing of such data is often impractical and in recent years, it has become much more common to attempt MR analyses using summary data estimates of SNP-exposure and SNP-outcome ...

... An array of sophisticated techniques exist for estimating the causal effect with individual participant data. However, the sharing of such data is often impractical and in recent years, it has become much more common to attempt MR analyses using summary data estimates of SNP-exposure and SNP-outcome ...

Selection Bias in Epidemiological Studies

... Controls in this study were selected from a group of patients hospitalized by the same physicians who had diagnosed and hospitalized the cases' disease. The idea was to make the selection process of cases and controls similar. It was also logistically easier to get controls using this method. Howeve ...

... Controls in this study were selected from a group of patients hospitalized by the same physicians who had diagnosed and hospitalized the cases' disease. The idea was to make the selection process of cases and controls similar. It was also logistically easier to get controls using this method. Howeve ...

Causal inference with observational data - Regression

... ability individuals may get more education, but would have had higher earnings regardless (leading us under this simple assumption to guess that the effect of education is overestimated). The selection problem can often be framed as a case of omitted variables (e.g. ability) or misspecification, but ...

... ability individuals may get more education, but would have had higher earnings regardless (leading us under this simple assumption to guess that the effect of education is overestimated). The selection problem can often be framed as a case of omitted variables (e.g. ability) or misspecification, but ...

Not a Sure Thing: Fitness, Probability, and Causation

... must give a satisfactory account of the relation between selection and drift. Selection and drift are discrete, discernible, complementary effects. Selection is the expected population change given the fitness distribution; drift is deviation from expectation. They are independent; selection can occ ...

... must give a satisfactory account of the relation between selection and drift. Selection and drift are discrete, discernible, complementary effects. Selection is the expected population change given the fitness distribution; drift is deviation from expectation. They are independent; selection can occ ...

General methodological considerations

... I wish to thank Alok Bhargava for organizing a discussion of this interesting paper by Adams et al. My discussion will focus on quite generic philosophical, statistical, and modelling issues that arise when inferring causal e0ects from complex panel and time series data. The analysis of Adams et al. ...

... I wish to thank Alok Bhargava for organizing a discussion of this interesting paper by Adams et al. My discussion will focus on quite generic philosophical, statistical, and modelling issues that arise when inferring causal e0ects from complex panel and time series data. The analysis of Adams et al. ...

The dilemma of causal inference in economics

... knowledge is itself not certain. It is often easy to build a number of theoretical models with conflicting conclusions, and quite generally theoreticians do not really trust one another’s assumptions. If we do not let theory guide us, we have no good reasons to believe that our causal conclusions ar ...

... knowledge is itself not certain. It is often easy to build a number of theoretical models with conflicting conclusions, and quite generally theoreticians do not really trust one another’s assumptions. If we do not let theory guide us, we have no good reasons to believe that our causal conclusions ar ...

Propensity Score - Hankamer School of Business

... – Get unbiased estimate of the job training program’s effects using randomized control group – Compare that with what you get by selecting a control group from the entire population that looks like the treatment group using various causal inference methods ...

... – Get unbiased estimate of the job training program’s effects using randomized control group – Compare that with what you get by selecting a control group from the entire population that looks like the treatment group using various causal inference methods ...

Power & Effect Size

... correlations are ‘significant’ This becomes relevant once sample size grows to 100~150 subjects per group Once you approach 1000 subjects, it’s hard not to find p < 0.05 ...

... correlations are ‘significant’ This becomes relevant once sample size grows to 100~150 subjects per group Once you approach 1000 subjects, it’s hard not to find p < 0.05 ...

View/Open

... assumption holds, then providing that we can account for the time varying confounding, associations between exposure and the outcome can be attributed unambiguously to the eﬀect of the exposure. Conditional on this assumption (which cannot be tested using the data), individuals’ exposure status at e ...

... assumption holds, then providing that we can account for the time varying confounding, associations between exposure and the outcome can be attributed unambiguously to the eﬀect of the exposure. Conditional on this assumption (which cannot be tested using the data), individuals’ exposure status at e ...

Package `experiment`

... This function estimates the average causal effects for randomized experiments with noncompliance and missing outcomes under the assumption of latent ignorability (Frangakis and Rubin, 1999). The models are based on Bayesian generalized linear models and are fitted using the Markov chain Monte Carlo ...

... This function estimates the average causal effects for randomized experiments with noncompliance and missing outcomes under the assumption of latent ignorability (Frangakis and Rubin, 1999). The models are based on Bayesian generalized linear models and are fitted using the Markov chain Monte Carlo ...

1 David A. Kenny APS Boston May 29, 2010 David A. Kenny APS

... .261 and is statistically significant (p = .005), with a small effect size (beta = .201). The difference between these two partner effects is not statistically significant (p = .454). Actor-Partner Interactions The actor-partner interaction for Husband Satisfaction is equal to -.214 and is not stati ...

... .261 and is statistically significant (p = .005), with a small effect size (beta = .201). The difference between these two partner effects is not statistically significant (p = .454). Actor-Partner Interactions The actor-partner interaction for Husband Satisfaction is equal to -.214 and is not stati ...

Analyzing a viral load endpoint in an HIV vaccine trial

... 8. Rosenbaum PR (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. The Journal of the Royal Statistical Society, Series A, 147, 656-666. ...

... 8. Rosenbaum PR (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. The Journal of the Royal Statistical Society, Series A, 147, 656-666. ...

A Spreadsheet for Analysis of Controlled Trials

... experimental and control groups for all transformations and back-transformations, to check on the balance of assignment of subjects to the groups. The purpose of a control group is to provide an estimate of the change that occurs in the absence of the experimental treatment. This change is then subt ...

... experimental and control groups for all transformations and back-transformations, to check on the balance of assignment of subjects to the groups. The purpose of a control group is to provide an estimate of the change that occurs in the absence of the experimental treatment. This change is then subt ...

Econometric Causality

... Counterfactuals are possible outcomes in different hypothetical states of the world. An example would be the health outcomes for a person associated with taking or not taking a drug. Causal comparisons entail contrasts between outcomes in possible states defined so that only the presence or absence ...

... Counterfactuals are possible outcomes in different hypothetical states of the world. An example would be the health outcomes for a person associated with taking or not taking a drug. Causal comparisons entail contrasts between outcomes in possible states defined so that only the presence or absence ...

Welcome to Statistics 111

... females) into treatment and control groups. • How many males will end up in treatment group? • Ideally, we would have 5 males in treatment group, and 5 males in control group (balanced) • However, there is a chance to get 9 males in treatment and 1 male in control group (unbalanced) Stat 111 - Lectu ...

... females) into treatment and control groups. • How many males will end up in treatment group? • Ideally, we would have 5 males in treatment group, and 5 males in control group (balanced) • However, there is a chance to get 9 males in treatment and 1 male in control group (unbalanced) Stat 111 - Lectu ...

Sucking Air: A Partial Review of Nancy Cartwright`s Hunting Causes

... any inference whatsoever as to causal relations. The alternatives are the same whether both variables are passively observed or one of the variables is experimentally manipulated. With sufficient sample sizes, in the absence of other information supporting exactly canceling pathways, I would make th ...

... any inference whatsoever as to causal relations. The alternatives are the same whether both variables are passively observed or one of the variables is experimentally manipulated. With sufficient sample sizes, in the absence of other information supporting exactly canceling pathways, I would make th ...

VEGF and Wet Age-related Macular Degeneration

... Enrolled subjects are randomly assigned to either VEGF Blocker or to PlaceboVEGF Blocker. Double-blinding is employed, so that Neither the subjects nor the study staff know the actual treatment status of the subjects in the study. ...

... Enrolled subjects are randomly assigned to either VEGF Blocker or to PlaceboVEGF Blocker. Double-blinding is employed, so that Neither the subjects nor the study staff know the actual treatment status of the subjects in the study. ...

Propensity score matching (PSM)

... One way of dealing with Z is to resort to first differences [Eq 7] E(y1)-E(y0)= β The problem with this “difference model” is that it attributes any changes in time to the policy Suppose something else happened between t=0 and t=1 other than just the program (eg. an economic recession/boom) We will ...

... One way of dealing with Z is to resort to first differences [Eq 7] E(y1)-E(y0)= β The problem with this “difference model” is that it attributes any changes in time to the policy Suppose something else happened between t=0 and t=1 other than just the program (eg. an economic recession/boom) We will ...

Propensity Score Weighting

... The IPW Estimators in Literature There are different methods to estimate the average causal effects (E(Yt)-E(Yc)) in the literature. 1. Rosenbaum and others (1998) proposed the following weighted estimators of the average causal effects. IPW2 is sometimes known as a ratio estimator in sampling lite ...

... The IPW Estimators in Literature There are different methods to estimate the average causal effects (E(Yt)-E(Yc)) in the literature. 1. Rosenbaum and others (1998) proposed the following weighted estimators of the average causal effects. IPW2 is sometimes known as a ratio estimator in sampling lite ...

Notes 25

... to a discontinuity of E[Y | X ] . For example, around age 65 individuals become eligible for a host of services and so comparing those a little younger and a little older than 65 may not allow researchers to infer the effect of one particular program. There are a number of things one can do. Most fi ...

... to a discontinuity of E[Y | X ] . For example, around age 65 individuals become eligible for a host of services and so comparing those a little younger and a little older than 65 may not allow researchers to infer the effect of one particular program. There are a number of things one can do. Most fi ...

Chapter 9

... Occurs when the relationship between an exposure and a disease outcome is influenced by a third factor, which is related to the exposure and, independent of this relationship, is also related to the health outcome Only the randomized experimental study allows us to balance out confounding among ...

... Occurs when the relationship between an exposure and a disease outcome is influenced by a third factor, which is related to the exposure and, independent of this relationship, is also related to the health outcome Only the randomized experimental study allows us to balance out confounding among ...