Questions tagged [treatment-effect]

A treatment effect is the causal effect of some "treatment" or policy intervention on an outcome variable. Such effects can be estimated with data from randomized or quasi experiments, and clinical trials or with observational data and methods for causal inference.

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166 views

Histogram variable combining both treatment and control group

I am doing an assignment for a Biostatistics course. We are asked very simple questions but there is one that seems strange to me. We have to plot the histogram of the blood pressure before and after ...
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How do I compare the effectiveness of an intervention in two groups?

I'm stuck trying to find a way to compare whether or not a given intervention has a greater effect in one group (Kids < 5) than another group (Kids > 5). The outcome I'm comparing is the incidence ...
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In causal inference in statistics, how do you interpret the consistency assumption in mathematical terms?

In causal inference, the consistency assumption states that there are no multiple versions of treatment. Specifically, for a potential outcome unit $Y_i$ and a binary treatment vector $\mathbf{Z}$, $...
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Why is the IPW (Inverse Probability Weighting) estimator unbiased when you know the propensity scores?

The IPW estimator, for outcomes $Y_i$, treatment $T_i$, and covariates $Z_i$ is: $$ \widehat{\text{ATE}}_{\text{IPW}} = \frac{1}{n}\sum_{i=1}^{n}\left[\frac{T_iY_i}{\widehat{\pi}(Z_i)} - \frac{(1-T_i)...
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What is the standard literature on Inverse Probability Weighting Estimators?

I understand that Imbens has several papers on IPW, but was wondering if there was a default text one would recommend to understand IPW. For example, where does the estimator: $$ \tau = \frac{1}{N^T}\...
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How to assess for balance of propensity score matching covariates in Stata?

I am comparing outcomes of a treated cohort (n=127) to a control cohort (n=732) using teffects propensity score matching in Stata. I did initial comparisons of baseline characteristics between the two ...
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392 views

For treatment assignment, what is the difference between Bernoulli assignment vs. completely randomized assignment?

I have read in literature several times a distinction being made between Bernoulli assignments and completely randomized assignments in assigning treatment (1) vs control (0) to units in a study. I ...
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1answer
970 views

Repeated measures in R with both independent and dependent variables changing

I searched many posts but could not find a solution to my particular problem. I would like to implement in R a way to measure the association between a dependent variable Y and an independent ...
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1answer
148 views

What type of regression analysis to use for data on economic experiment

I'm currently performing analysis on a dataset I've retrieved by running an economic experiment. Subjects were either in treatment 0 and 1 and had to perform 'real effort' tasks. That is, they had to ...
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Clustered data in ophthalmology, which statistical test to use?

I am doing a retrospective chart review comparing old treatment regimen (120 shots of laser) to a new treatment regimen (160 shots of laser). The main outcome measured is intra-ocular pressure (i.e. ...
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558 views

Difference in difference model with multiple time periods and treatment/control pairs

I am trying to estimate a difference in difference model with 10 time periods (t=10) and many different plots of land, which are the unit of observation. Each plot of land which becomes treated at ...
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Propensity score matching on panel data with treatment varying by periods

I have a panel data of 200 individuals around 100 weeks. A latent ability issue for individual may cause the estimation for one IV biased. This IV is continuous variable. Except for panel fixed-...
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Cox Model vs. Logistic Regression for comparing two treatments? [closed]

I am trying to choose which statistical test(s) to use for my study. It is a retrospective cohort study comparing the outcomes of 2 different surgical oncology techniques (Let's call them Treatment X ...
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Logistic regression to adjust for confounders in treatment effect estimation: when is my model satisfactory?

I am trying to measure the effect of a treatment on a binary outcome using observational data. However, the group that was treated and the group that was not treated are not equivalent: assignment to ...
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How to estimate Complier Average Causal Effect?

In an experiment, in addition to the assignment for every experimental unit, you also know the identity of some of the compliers, but not all. How to use this information to estimate the causal ...
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1answer
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Significance calculation between two sets of before and after measurements. Does one decrease more than the other?

Some background: I am studying the immune system. This is the defense the body has to protect itself from pathogens like bacteria and viruses. The main cells involved are commonly known as white ...
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Interpreting DD estimates in terms of their economic magnitude

I'm currently doing the statistical part of my final thesis and I am having troubles with correctly interpreting my regressions results. The models I am using (with the plm package in R) are pretty ...
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ATT vs ATE in propensity score matching when using DiD estimates

According to Lee and Little 2017, when using propensity score (PS) methods, weighting on odds will generate the Average Treatment Effect on the Treated (ATT), while using subclassification and ...
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767 views

Computing and interpreting treatment effects for binary outcome using multiply imputed and matched data

I am using genetic matching, implemented through the MatchIt package (dependent on Matching and ...
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Combing panel data with cross -section counter-factual?

I have a panel dataset of a treatment group with no control group panel. It has been proposed to me that I use cross-section from a a national survey, taken at baseline and same time as post-...
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Proving equation 3.3.12 in Angrist Pischke Mostly Harmless Econometrics (inverse probability weighting formula for ATT effect)

On page 82 in Mostly Harmless Econometrics, there is a formula for the average treatment effect on the treatment, using propensity score, that is $$\tag{1} E(Y_{1i} - Y_{0i}\vert D_i =1) = E\bigg[\...
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Calculating the CACE using instrumental variables

In randomized trials with non-compliance among the treatment group, a common estimator is the Complier Average Causal Effect (also called the Local Average Treatment Effect), which (conditional on a ...
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979 views

Difference-in-difference model with mediators: Estimating the effect of different elements of a policy

How do I conduct a mediation analysis in a difference-in-difference setting? For example, a city selects some neighborhoods for a new crime fighting strategy (the treatment $D$) that involves an ...
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Before and after effect with logistic regression, what method to use?

So I have a test subject A which consist of a binary variable 0 and 1. I want to see if this binary variable is decided by some independent variables (plus interactions) which are continuous, which in ...
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561 views

Panel data regression on the effect of a treatment without control group

I have a panel data set. The dependent variable is a certain numerical score for each individual and time period. I have a number of independent variables that vary by individual, by time, or by both. ...
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IV: Average Treatment Effects

I am estimating an instrumental variable regression (IV) where Y is my dependent, X my variable of interest, and Z the dicotomous instument. I am interestend in verifying if the coefficient estimate ...
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1answer
988 views

Effect size and standard error for Mann-Whitney U statistic

I'm conducting a meta-analysis and one of the studies only reports Mann-Whitney $U$ statistic. The table with results is pasted below. Circled in red is the $U$ statistic that tests the differences ...
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I don't know how to calculate SS(A), SS(A|B), SS(A|A*B), SS(A*B|A,B), etc

I read gung's answer to "How to interpret type I, type II, and type III ANOVA and MANOVA?". Instead of messing with R I'd like to just create my own linear model in a spreadsheet. The problem is that ...
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1answer
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ANOVA. Let α β γ δ be regressors. If you discuss the interaction α*β*γ, can you discuss β*γ*δ too?

I've been taught that if you discuss an interaction of the regressors α and β, then you can't discuss the simple effects (α alone and β alone). I guess you can't even discuss αβ if you discussed αβγ. ...
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Adjusting for baseline as covariate in observational studies

The scenario is regarding treatment effect in an observational study (i.e. not randomised): those given the treatment would be more unwell at baseline. A clinical trials statistician suggested adding ...
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Matching / imputing between experimental and control group on a specific (i.e. latent) variable

I am currently researching the impact of survey participation on subsequent behavior, i.e., does merely participating in an intention survey about fitness products influence post survey spending. As ...
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342 views

Longitudinal measures mixed model in lmer in R

I would like to build a mixed model using the lme4 package in R. The study design is like this: We have measured the change in a variable over time in mice under different Diets. The mice under ...
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Difference ATE and ATET?

I have some problems understanding the difference between ATE and ATET and the Selection Bias. To explain what my understanding is I have done the following representation so you can correct me: We ...
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2k views

Propensity Score Matching with Panel Data

I have a Panel Data Set from 2000 to 2013 and I want to use Propensity Score Matching to analyze it. The treatment variable varies between individuals over time, an individual can get treated any time ...
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Which Theories of Causality Should I know?

Which theoretical approaches to causality should I know as an applied statistician/econometrician? I know the (a very little bit) Neyman–Rubin causal model (and Roy, Haavelmo etc.) Pearl's Work on ...
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799 views

Continuous Treatment effect to assess the impact of Early vs Late treatment

I have an observational dataset with only treated observations with different ages, but treated at different times for 7 years. Each treatment had different durations; from 2 days to 160 days. The ...
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603 views

Analysis strategy for rare outcome with matching

I'm working with a dataset of ~100,000 individuals where ~500 (0.5%) individuals received treatment. I have several continuous and count outcomes for all observations that I would like to compare ...
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Is my experiment design and interpretation of treatment effect fine?

Suppose I want to measure the effect of a treatment in a group of schools (my population). I randomly select a sample of schools and two classes for each school (one treatment and one control). Would ...
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1answer
298 views

Test if treatment effect is different between samples

I want to determine if there is significant variation in the effect of a treatment amongst my replicates. The treatment effect is measured as a proportion. Let me describe: I study heart defects (in ...
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1answer
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Estimating Equations for Treatment Model in Treatment Effects Estimation — How is this Equation Derived?

While reading the STATA 14 Treatment Effects Reference Manual (http://www.stata.com/manuals14/te.pdf), I'm having difficulty understanding how they arrive at the equation for the treatment model, that ...
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32 views

Effect plot contradicting regression coefficient

Among other parameters, I examined the effect of the factor $Q$ on the response variable $Y$. I found a linear model with $R^2$ and adjusted $R^2$ being around $0.95$. The $F$ statistic of the overall ...
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55 views

Population Treatment estimate from multiple uneven groups

I have a number of different groups (on the order of 100). Each has many members (on the order of 1000) but the groups are all different sizes and range from 100 to 1000+ in size. Each of these ...
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573 views

Instrument Variables and Exclusion Restriction from a Mediation perspective

I'm having trouble making sense of the exclusion restriction in instrumental variables. I understand that the unbiased treatment effect is $B = \frac{Cov(Y, Z)}{Cov(S, Z)}$, where $Y$ is the outcome,...
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1answer
68 views

Is difference-in-difference (did) an adequate model for this?

I am looking into whether fuel economy standards in the US (Wiki) have an influence on the footprint of a car (footprint is the area between the four wheels). The fuel economy standard depends on the ...
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1answer
129 views

Designing a hypothesis experiment for cost reduction

I have two groups of samples. Control group and treatment group, treatment group gets a special training. This training will cost me $X$ dollars per person. This training is supposedly reduce the ...
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How to deal with treatment effects that occurs multiple times at different time for each individual ID

I've been searching for a long time for this issue and couldn't find the proper answer so I post a question here. Here I put the data set example first. ...
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1answer
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What is the optimal small-sample experimental design for multiple treatments and costly trials?

I have an experimental outcome in three outcome variables, y1, y2, y3. My objective function is: a * ln(y1 - y1*) + b * ln(y2 - y2*) + ln(y3 - y3*) for a, b, c, y1, y2, y3 >0. The starred ...
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2k views

One-to-many matching of propensity scores and average treatment effect on the treated

I've been working on a propensity score matching project, but having no stats classes under my belt I'm struggling to calculate the so called "average treatment effect on the treated" (ATT) so that it ...
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1answer
6k views

Dynamic treatment timing in a panel-DiD framework

I have a question regarding the timing of treatment effects and how one could use the difference-in-difference estimator on a panel data set. Let me begin by saying that I have a big firm level ...
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Which is the best approach? Difference in Difference with Heterogeneous Effects

I am interested in the performance of two competing approaches to estimate a treatment effect, for which one would expect heterogeneous effects. The point of origin is a treatment conducted from ...

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