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 ...
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 ...
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 ...
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 ...
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
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. ...
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
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
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
... 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 ...
... 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).
The actor-partner interaction for Husband Satisfaction is equal to -.214 and is not stati ...
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 ...
... 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 ...
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 ...
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
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 ...
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 ...
... 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
There are a number of things one can do. Most fi ...
... John L. Loeb Professor of Stats
... 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 ...
Rubin causal model
The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name ""Rubin causal model"" was first coined by Rubin's graduate school colleague, Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments. Rubin, together with other contemporary statisticians, extended it into a powerful general framework for thinking about causation in both observational and experimental studies.