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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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Propensity score matching: bias adjustment

I'm using propensity score matching to match similar individuals. I.e., I first estimate a propensity score (the probability of treatment conditional on some set of variables) and then match on the ...
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Comparing two difference-in-difference models when making the treatment group larger

I have a question regarding some potentially existing empirical tests in the difference-in-difference context. Assume that I have the usual setting of observing some hypothetical outcome for a ...
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Sample selection in difference-in-differences

I have a dependent variables with a significant amount of zeros (and the share of zeros is different between the control and treatment groups, and changes between the pre- and post-treatment periods). ...
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Cluster RCT with different timing and exposure to treatment

We are currently evaluating a government Microenterprise program using a cluster RCT design. The treatment involves the provision of a grant to a poor household to start a micro-enterprise. We are ...
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Best way to measure treatment effect across two treatments

I have two sets of patient-level data for two distinct treatments, unfortunately, the data is only over a 14 week period and I'm hoping to build a predictive model to estimate/simulate what the data ...
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Treatment (equivalent to experimental groups) in Experiment as Fixed AND Random Effect in Mixed Model Linear Regression

I have data from a sociology experiment with three groups. Each group is equivalent with a different treatment for a subject (n=700). The treatment were surveys, differing in the amount of information ...
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Testing two potential interaction variables (or potential sources of effect heterogeneity) against each other

I have an experiment I have run, and I am testing for heterogeneous treatment effects (pre-registered and not fishing for any particular result!). Let's call the outcome $Y$ and the treatment variable ...
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Deterministic Assignment to Treatment

When estimating causal effects, you want to compare individuals as similar as possible. It is from this need that stems the exchangeability (/ignorability) or conditional exchangeability (/ ...
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Do propensity score matching methods need to factor in the index date in a matched cohort context?

I am working on a comparative effectiveness study where we estimated the propensity of treatment between two groups and are exploring matching on the propensity score. The study period is long, ...
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IV-estimation vs. Heckman's selection model

I am trying to grasp the difference between IV-estimation and Heckman's selection model. I do that by considering the following set-up. My outcome if interest is y ...
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Shortfalls of using Machine Learning algorithms to predict counterfactual outcome of the treated

To see the effect of a treatment we would like to know the difference between a person in a world in which she received the treatment and one in which she does not. Since we never observe the ...
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Difference-in-Differences combining 3 periods prior to, during and after treatment

I have Difference-in-Differences models with 3 periods: pre-treatment, treatment and post-treatment periods. Normally, we could do DID in the following way: run DID between pre-treatment and ...
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Propensity score matching: covariate balance

I have one concern about propensity score matching's assumption. It seems that what propensity score is doing is to say that the choice of treatment depends on pre-treatment covariates. Suppose I am ...
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Model subgroup- and covariate-specific effects for binary outcome over time

I am currently planning an analysis, in which I try to separate the change in the level of a binary outcome into a subgroup- and a covariate-related effect. Let's say there are three kind of ...
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Identifiyng mutual trends between treatments

(WARNING: Not a statistician - do not get mad) We have two different treatments $A,B$, each was tested separately ($A$ treated group vs control, $B$ treated group vs control). In each comparison, the ...
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1answer
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Variance of ATE (Average Treatment Effect) from log-linked gamma model

I have matched my sample using propensity score matching such that each individual has an estimated propensity score of being assigned to a treatment group. Let $T_i$={0,1} be the actual treatment ...
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Is there a commonly accepted effect size parameter for pairs of Bernoulli processes?

Let $f(x|\rho)$ be the Bernoulli pmf with probability $\rho$ of success. \begin{align} f(x|\rho) = \left\{ \begin{array}{ll} \rho & x = 1 \\ 1-\rho & x=0 \end{array} \right....
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Looking for methological advice (“decomposing” treatment effect)

I'm looking for methodological advice on the undertaking described below. I tried to simplify as much as possible and hope its comprehensible this way - if not, please pardon my ignorance and shoot ...
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Difference-in-differences with unbalanced panel data

I am working on a quasi-experimental study with a large unbalanced panel dataset. There are N=300,000 and T=20, where roughly 50,000 individuals receive treatment since several different periods. I ...
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Diff-in-diff with mactched control group

I want to run a diff-in-diff model. To choose an appropriate control group, I use a nearest-neighbor matching model based on several determinants of the outcome variable that I study. I was ...
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Diff-in-Diff with multiple treatment groups

I have monthly panel data and I want to estimate the effects of two different treatments that occur in different time periods. The treatment groups are not the same. An individual can belong one, both ...
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1answer
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Average treatment effect using relative risk difference?

So I'm designing a RCT to evaluate the effectiveness of a smoking cessation intervention. For my analysis I've decided to look at three different things. 1) the point prevalence of smoking cessation ...
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Conditional treatment effect and average treatment effect under no unmeasured confounders (ignorability)

The conditional treatment effect (CATE) is defined as: $$ \tau(x) = \mathbb{E} \left[ Y^1- Y^0 \mid X = x \right], $$ the average treatment effect (ATE) is defined as $$ \tau_{ATE} = \mathbb{E}\...
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Diff in diff model with multiple treatments in multiple perdiods?

Can I estimate a diff in diff model to compare the effects of two different treatments that apply in different time periods in different countries? I have 30 countries for an average time span of 34 ...
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IPTW ATE Significance Test

Does anyone have any experience using propensity weighting schemes such as IPTW (Inverse Probability of Treatment Weighting) estimation? I have a model that uses IPTW to estimate the Average Treatment ...
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Experimental Analysis with Several Discrete Treatments

I am analyzing data that are originating from an randomized experiment and I am new to this. There was one control groups and three different treatment groups. The treatment groups are discrete and ...
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What statistical tests can I use? Repeated measures design, two groups, each is control and experimental at different points in time

I need to determine if an intervention had an effect. In the experimental design, measures were taken at three points in time: pre-test, post-test #1 and post-test #2. One group (group A) received the ...
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Marginal independence sufficient for showing that ATE is identified?

I have a basic question on the relation between counterfactual outcomes and treatment. In what follows ATE means average treatment effect. Notation: Let $D$ denote the treatment, taking value in $\{...
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Marginal independence sufficient for showing ATE=ATT?

I have a basic question on the relation between counterfactual outcomes and treatment. Notation: Let $D$ denote the treatment, taking value in $\{0,1\}$ where $D=0$ means untreated and $D=1$ means ...
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1answer
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Understanding the perfect randomization assumption in treatment models

I have a basic question on the relation between counterfactual outcomes and treatment. Notation: Let $D$ denote the treatment, taking value in $\{0,1\}$ where $D=0$ means untreated and $D=1$ means ...
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Randomized Treatment within treated group

I don't know if this situation has a particular name. I'll just give the example and my question. Suppose we have two naturally concurring groups, $A$ and $B$. By that, I mean there is a naturalistic ...
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2answers
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Mixed ANOVA vs Ordinal logistic mixed model?

I have two different groups, Treatment A vs Treatment B, with measurements for each individual in four different time points. That is a 2 x 4 design. The dependent variable is a discrete scale from 1 ...
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ANOVA or test to distinguish treatment groups [closed]

I have two lots of questions which should be pretty straight forward to solve with an ANOVA or as studies previously used Wilcoxon-Mann Whitney matched pairs. However I'm not quite sure what to tell R....
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Which statistical test to compare between and within differences?

I have two groups of patients with two different treatments applied, Treatment A vs Treatment B. In addition, I have measurements on 4 different time points for an ordinal variable (scale 1 to 4, ...
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When is it valid to use race/ethnicity in causal inference?

It seems that often in social science, race is examined in causal terms, as researchers are interested in the differences between various ethnic groups in outcomes when controlling for other ...
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Interacting treatment variables with independent variables in matched samples

Can we interact the treatment variables with independent control variables that have been used for matching? I have a binary treatment variable called 'medicine'. It indicates whether a person ...
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Parallel trend assumption in difference-in-differences

I want to run a difference in difference model to evaluate the impact of a policy change using panel data with 30 individuals in the control group and 35 individuals in the treatment group. When ...
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Evaluating the effect of an intervention using panel data when comparison groups are characteristically different

I am working with a panel dataset generated in four waves (Round 1, Round 2, Round 3, Round 4). After the first wave of data collection and before the second, an intervention was introduced by the ...
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Temporal test for treatment when control is zero

I have a 5-year time series of abundance for three replicates in response to treatment, and three control replicates. ...
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2answers
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Which weighting to use for regression analyses at different levels of aggregation

I run a study with subjects in 400 groups of heterogeneous sizes ranging from 2 to 20 individuals. I have outcome data at the group level and at the individual level. Treatment was randomly assigned ...
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1answer
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Average treatment effect in binary choice model

All the random variables below are defined on the same probabiluty space $(\Omega, \mathcal{F}, \mathbb{P})$. Consider the following model $$ Y\equiv 1\{\epsilon > \beta_0+\beta_1X\} $$ where $1$...
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Pre-treatment period in difference-in-differences model

I want to evaluate the consequences of a policy change using a diff-in-diff setup. I have quarterly data over ten years before the treatment ($t_{-10}$, $t_{-9}$, ..., $t_{-1}$) and ten years after ...
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Composition of groups in difference-in-difference models

Assume a difference-in-difference setup where the control and treatment groups are heterogeneous (different observable characteristics) but The parallel trend assumption is verified in the pre-...
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1answer
32 views

Interpretation of a dif in dif approach

I am conducting a Difference in Difference Regressionalaysis of a Treatment effect over time. Leaving out the covariates, I have three variables over which I am conducting a multiple linear ...
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Pre-Post control impact analysis with multiple observations

I have pre-post data on a test being done on 2 cities (one is a control-one is the treatment). I need to analyse this to check if there is a significant change observed in the treatment city. Data is ...
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Post dummy in multiple groups and time periods DiD estimation

I have a question regarding an answer of another question posted here. My dataset seems comparable and has different policy treatment timings for different firms. The answer outlines how to construct ...
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Performing a two-way ANOVA, I get a non significant Levene's and a significant interaction effect. What it the next step?

In the case described above, our Professor instructs as to interpret the simple effects by using MSwithin of the two-way ANOVA. In case the Levene's test is significant though, he instructs us to ...
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1answer
93 views

Testing for treatment effect hetereogeneity in regression discontinuity design

I am not a statistician or econometrician, so please bear with me. For a term paper, I am estimating local treatment effects using a regression discontinuity design, and I want to test whether the ...
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1answer
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Is augmentation of treatment effect caused significantly by mediator?

I want to find out if the influence of potential mediator P on treatment effect D is significant. In the two models below $\beta_1$ and $\lambda_1$ are different and I don't know how to test if the ...
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etregress Stata - treatment effect model (twostep and ML estimation)

I have a panel of 10 years of data and 3000 different IDs. I would like to fit a treatment effects model to study the impact of specific type of regulatory warning (modeled as a dummy variable equal ...