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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When does a difference in means not capture the true treatment effects vs a regression with pre-treatment controls?

A question from Gelman - Regression & Other Stories... In answer to my own question...my understanding is that a difference in means should not capture the treatment effect when there are pre-...
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Stata teffects ATET

How does stata estimates ATET using teffects psmatch. I understand the average treatment effect (ATE) is computed by taking the average of the difference between the observed and potential outcomes ...
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Question about using potential outcomes in DAGs in real world example

I am trying to understand how DAGs and potential outcomes look together. I came across these excellent posts (here and here, but I am trying to understand how this looks in a real world example. ...
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Balance diagnostics: Why not measure post-matching balance **within** matched treatment-control pairs?

I understand there are a number of techniques for evaluating post-matching balance at the covariate level: standardized mean difference (SMD), variance ratios, and empirical CDF statistics. Are there ...
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Difference-in-differences if the control group is treated later

Would a difference-in-differences analysis still tell me something important if the control group was treated later in time? Or, would I be better off only restricting my analysis to the time frame up ...
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Why is SATE different to Treatment effect (difference in means)?

A question from Gelman - Regression & Other Stories. This one has me a bit stumped, I've reread sections of the chapter and I'm still not understanding why this is the case. I think the answer is ...
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How is a difference-in-differences model represented in a causal diagram (or directed acyclic graph)?

Unlike a standard causal model with A = Treatment, X = Confounder, and Y = Outcome: a difference-in-differences (DiD) model is concerned with estimating the Average Treatment Effect on the Treated (...
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Seeking help identifying the right causal model for my research

I am conducting a quasi-experimental research where the treatment is of varying intensity. For e.g., to investigate the impact of CEO's transgression on the stock market behavior (hypothetical; not my ...
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Causal Inference for experiment

I'm working through a textbook (Regression and Other Stories) and have come across a particular problem that I am having difficulty convincing myself I understand. I am specifically interested in part ...
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Struggle with use of event study approach with non-firm data

I am attempting to use event study combined with difference in difference analyses to assess the parallel trend assumption in an individual level analyses of the impact of COVID on monthly income. The ...
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Cohens d in relation to a single case

Cohen's d in relation to a single case The average effect size (Cohens d) for insomnia-treatments ranges from about 0.3 to 0.4. If I had a patient that suffered from insomnia 4 out of 5 weekdays (...
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How was effect size calculated in this study?

I am trying to learn about effect sizes and how it relates to real-world data so I looked at a random study for insomnia-treatment: https://onlinelibrary.wiley.com/doi/10.1111/jsr.12067 and the ...
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How to compute Cohen's d and p-value for comparison of least squares mean from two different mixed models?

I have two randomly assigned treatment interventions (A and B), and two subgroups of individuals (X and Y). This is a longitudinal study with dropout. I want to test whether the outcomes of ...
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What are weights in coarsened exact matching? [duplicate]

I am using MatchIt in R to estimate the treatment effect on the treated (ATT) using Coarsened Exact Matching. Here's a replicable example of what I'm trying to do: <...
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Inverse Propensity Score Weighting vs. Double Machine Learning

I am familiar with Inverse Propensity Weighting (IPW) for the estimation of causal effects, and recently, I came across the 2016 paper by Chernozhukov et al. on Double/Debiased Machine Learning. From ...
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Distinction between Treatment and Control Group

I am struggling to understand how to interpret the treatment and control group in the following case of DID regression. We need to estimate the causal effect of brick kilns on downwind PM2.5 monitor ...
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Impact evaluation: non-traditional regression equation framework

I have a panel data of households. Meaning I have records of many households over multiple weeks. For the first six weeks no household received any treatment. For next six weeks all households ...
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Continuous variable Treatment Group in DID Regression [duplicate]

I am struggling to understand how to interpret the treatment and control group in the following case of DID regression. We need to estimate the causal effect of brick kilns on downwind PM2.5 monitor ...
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Simulating Data for Factorial Design of Hormone

I am new to simulating data and want feedback on the proposed simulation given the biological relationships I am trying to simulate. Did I make a good model to simulate this data or can it be improved ...
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ATT vs ATE vs ITE

I was wondering what considerations should be made when choosing an appropriate estimator for treatment effect. E.g., suppose you have experimental data on email promotions, such that 1/3 of your ...
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Inference for the average treatment effect for Bernoulli trial

In the book of Imbens and Rubin, they discuss four common classes of assignment mechanisms that fit into this framework: Bernoulli trials, completely randomized experiments, stratified randomized ...
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Is this derivation in Manski (1990) correct?

Consider the following setting. There are two treatments, $A,B$. Individuals in the population are described by a tuple $(y_A,y_B,z)$ where $z \in \{A,B\}$ denotes the treatment received. Only $y_A$ ...
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Is parallel trends assumption necessary in difference-in-differences analysis?

Reading the literature on the subject, I haven't encountered clear reasoning why the parallel trends assumption must hold. In fact, there have been recent papers on ways to relax this assumption (see ...
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Can using 4/5 years averages as regressors cause endogeneity?

I have a panel related to political election. Id is country and time is NOT the year but the election event sequentially ordered (first election in the country, second...and so on), so that you have, ...
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Interpreting Treatment Effects from R Logistic Regression

This is probably a simple(ish) question but I would appreciate any help/pointers. I have the following logistic regression model: ...
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Is there a difference between ignorability and strong ignorability?

The whole idea around ignorability is still leaving me a bit confused. I did read this post from the site: Strong ignorability: confusion on the relationship between outcomes and treatment. It had ...
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Estimate ratio of treatment effects from DiD

I have a difference-in-difference model similar to this: $$y = \beta_0 + \beta_1 \text{treated} + \beta_2 \text{post-treatment} + \beta_3 \text{treated} \cdot \text{post-treatment}.$$ The outcome, y, ...
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Does the variance of regression coefficient in multiple linear regression increases if we replace one of the predictors with its immediate parent?

We have a data generation process as follows: \begin{align} Z_1 &= E_{Z_1}\\ Z_2 &= \theta_{Z_2} + \theta_{Z_1 Z_2} Z_1 + E_{Z_2} \label{Z2BasedOnZ1}\\ T &= \theta_{T} + \theta_{Z_1 T} ...
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Difference-in-differences with a continuous treatment

I am currently doing research about the effects on the labour market of Venezuelan migration in Peru. For the first step, I want to get the effects of natives' mean wages in the three biggest cities ...
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Weights for estimating ATE (rather than ATT) in SAS %CEM macro for coarsened exact matching?

I'm running Gary King's %CEM SAS macro (available here) for coarsened exact matching (CEM) for a project at work. The macro works fine for estimating the Average Treatment Effect on the Treated (ATT) ...
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Measuring the treatment effect of a binary variable on a binary outcome in R

I have a data set with 10000 entries of projects that take part in an auction for financial support. In that auction all of the bids below a certain cutpoint receive the support. The data includes the ...
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Test for effects specific to single groups in multiple group testing

I measured about 20.000 variables (let us say they are all normally distributed for simplicity) for one control group and 4 treatment groups. Now, I would like to answer the following question: Which ...
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Calculation of ATU (aka ATC) instead of ATE in Titanic dataset in Causal Inference: The Mixtape

I think there are some mistakes in chapter 5 of the book Causal Inference: The Mixtape by Scott Cunningham (2021), but I want to check that I'm not just misunderstanding. The book is available for ...
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Can you split a very large sample into smaller ones to get more observations?

Suppose you are running an experiment on the entire population of the US. Every day you alternate giving them some treatment and you observe some index at an aggregate, country level. Note that for ...
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causal effect in linear causal model

I have the following linear causal model: $B = \epsilon_B$ $C = \epsilon_C$ $A = \beta_1 B + \epsilon_A$ $Z = \beta_2 B + \beta_3 C + \epsilon_Z$ $D = \beta_4 C + \epsilon_D$ $X = \beta_5 A + \beta_6 ...
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What should we do if we find an anticipation effect in difference-in-differences?

When using a difference-in-differences estimator, one key assumption is the absence of anticipation, meaning that the real event date shouldn't be a couple of years before the real event year (yearly ...
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Causal inference where potential outcome is somehow "violated"?

The fundamental problem of causal inference says that only one potential outcome is observed for each unit. What happens if both outcomes from control and treatment can be observed? Can we still make ...
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Conditional Average Treatment Effects

Suppose I am interested in understanding the effectiveness of two type of medicine, A and B. In the usual setting, each patient is considered an experiment unit, and is assigned one of the medicine. ...
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Difference-in-Difference with two control groups and one treatment group over the same period of time using RStudio

So I'm trying to run a regression for one of my economics classes with one treatment group and two control groups over a period of time. I'm currently trying to create a dummy (binary) variable to ...
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Compare effects of a treatment across groups

I am trying to conduct a retrospective study to understand the effect of the presence of heart disease (N and Y) on binary categorical outcomes of certain types of surgeries (let's call them A, B, C). ...
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Estimation of Treatment Effect using Bayesian Nets

I am trying to estimate a causal effect using DAGs in R. While by now I can fit baysian nets, draw DAGs, and can validate the independence conditions of my models, I still have no clue how to ...
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Event study / DiD with panel data and repeated treatment in different years for each country

I have an unbalanced panel dataset with approx. 30 countries from 1980-2000. I would like to study how political uncertainty measured by close elections affects a certain continuous variable Y, say ...
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8 votes
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How to design experiment and holdout for two types of treatment at the same time

Let's say there are two types of treatment, namely treatment A and treatment B. A subject can be in one of these categories: get treatment A and then treatment B. get treatment B and then treatment A....
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Regression Coefficients in terms of Individual treatment effect

According to the famous book “Mostly Harmless Econometrics”, the regression coefficient can be written in terms of the individual treatment effects. Precisely, the authors state that: Even so, ...
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Identification of structural parameters in a linear model (treatment effect context)

Suppose that we have $N$ observations indexed by $i=1,...,N$. The observations are partitioned in three groups indexed by $g=1, 2,3$. Here, we consider potential outcomes $Y_{ig}^0,Y_{ig}^1,Y_{ig}^2$. ...
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Treatment and control group balance in ATE estimation with regression

I have a (probably) stupid question but I am still actively learning about causal inference and maybe I am not getting how the pieces do connect together. I came across different methods for ...
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Triple difference-in-difference with continuous treatment

I am struggling to understand how to interpret a triple DiD with one continuous treatment. I found Olden and Møen (2020) useful, but it only considers the case of two binary treatment variables. ...
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Testing difference in coefficients for two treatment conditions in separate models with shared control group

Subjects were randomized into treatment and control groups. Within the treatment group, subjects were randomized to receive the treatment under condition A or B (mutually exclusive groups). For all ...
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How can i find the variance of ATE by using g computation without bootstraping?

I have a binary treatment and binary outcome and have estimated the average treatment effect (ATE) by using g-computation, which involves training a model for the outcome given the covariates and ...
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Estimation of ATT and average untreated effect on the untreated by OLS estimation

Suppose there are a binary treatment. Based on the treatment, we can consider a treatment group and a control group. Here, assume the following data generating process of potential outcomes: \begin{...
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