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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Estimation of two successive treatments - Differences in Differences

I am trying to estimate the effect of two successives policies on individual subjects. I have a panel data following individuals each 3 years from 2005 : 2005, 2008, 2011, 2014 and 2017. The first ...
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How to estimate which user features predicts the treatment effect of an AB test

I have an AB test with a target variable (Y), M user features and N other features. The user ...
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Panel data regression with time varying treatments and fixed effects

Experts, I have some trouble concerning my regression model for a panel data analysis. The dataset includes observations of 200 firms over a period of 6 years (2000 - 2005) regarding merger activities ...
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How to test for pre-trends in a DiD setting with repeated cross sectional data?

I have a dataset consisting of repated cross sections spaced out every two years (e.g, 2008, 2010, 2012...). I am running a DiD specification to identify the causal effect of treated individuals after ...
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Is "triple differences" and estimating "heterogeneous treatment effects" the same?

I am running a difference-in-differences (DiD) regression with a time and treatment interaction and I have a continuous outcome variable. However, I hypothesize that the DiD estimator can be moderated ...
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How can i calculate the relative ATT (as percentage) using propensity score matching method?

I am using a propensity stratification method (in dowhy package). below is a table of my data. It splits at each strat the number of users in drug population vs not. Note that the strata are split ...
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Positivity assumption in causal inference with continuous covariates

In causal inference, studies usually require several assumptions (e.g., Unconfoundedness) to make valid causal statements. One of these assumptions is the 'Positivity' Assumption (sometimes referred ...
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In DoubleML how is model hyperparameter chosen?

I'm referring to DoubleML pkg's example in python here or R here, for example cell 27 where 'set_ml_nuisance_params' was used to set model hyperparam. For those familiar with the package, how are ...
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post-double selection lasso (pdslasso) on multiply-imputed data

I would like to use Belloni, Chernuzhukov and Hansen's approach to selection of controls when estimating treatment effects (2014, REStud) on data with a significant number of missing observations (15-...
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What are the pitfalls of matching on the outcome variable pre-treatment?

Are conducting propensity score matching to compare the effect of treatment in a balanced sample, however one of the factors associated with the assignment of treatment is previous levels of the ...
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Estimation in judea pearls book - how is estimand estimated from observed data?

I would like to ask a few questions on Judea Pearls approach to causal inference. In judea pearls book we want to estimate: P(Y|do(X)) now to get to the estimand ...
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Econml summary() interpretation [closed]

I just started using EconML library, I would like to ask what does the output of inference summary mean? what exactly is the prediction/inference doing here? UPDATE: Also, I don't understand about the ...
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Probe interaction between treatment integrity (fidelity) and treatment effect in RCT with blank/placebo controls

my friends at Stackexchange, I have been contemplating and searching the literature for the best options to probe the interaction effect between treatment integrity (or fidelity) and treatment effect ...
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Discordant results using IPW and overlap weights (propensity score approach) in sub-group analyses

I am investigating the effect of a treatment on the risk of a disease (% disease=18.5% (515/2784)). To do so, I use a propensity score (PS) approach with two different weights methods: IPW (stabilized ...
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In an RCT, does running OLS on $Y_i = \beta_0 + \tau D_i + \varepsilon_i$ and recovering $\tau$ recover ATE or ATT

Let's say I run an RCT and then run OLS on $Y_i = \beta_0 + \tau D_i + \varepsilon_i$ where $D_i$ is a dummy variable indicating whether an individual $i$ received the treatment. If I were to take the ...
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Modeling treatment effect with different drug classes and repeated measures data

I have a repeated-measures dataset in which a person having an adverse event is treated using a drug from a certain class. The drug classes are Level 1, 2, or 3, with increasing level representing ...
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How does Generalized Random Forest calculate the gradient of the score function?

The reference is GENERALIZED RANDOM FORESTS by ATHEY, TIBSHIRANI and WAGER (2019). They construct a general algorithm to grow trees and forest for estimation of target parameters that are conditional ...
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Bootstrapping necessary in IPTW? [duplicate]

IPTW as in inverse probability of treatment weighting. Currently I am working on a project investigating the ATT of certain antiviral, and I found out there are several stats papers on the necessity ...
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Predicting therapy response from categorical variables

We have a dataset of about 200 patients that have been diagnosed with a lung disease. At the time of diagnosis, their lung capacity was characterized by various parameters, all of them measured on a ...
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How to test a treatment had a positive effect on the experimental group?

For the experiment, 12 people were invited. All participants were pre-tested (10 tasks with 0/1 marks). Then we randomly divided all the participants into two equal groups of 6 people: experimental ...
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Why I can recover the total effect with OLS but not with logistic regression?

The dataset ISLR2::Default contains one observation per individual. The variable Default is "Yes" if the individual ...
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Common Trend Assumption in Difference-in-differences

I'm running a DiD analysis on whether the introduction of a spinoff NFT (non-fungible token) collection affects the prices of the parent NFT collection. First thing that I did is to visually test ...
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Calculate sample size for clinical trial without raw data

Usually, we calculate the sample size needed for a clinical trial based on a former clinical trial which shows there is a significant effect. What if there is no such a trial? What if there is no ...
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How does one define what measure to use for a Regression Discontinuity design?

For example I am looking at the effectiveness of a reading learning program for 3rd graders. How do I define an acceptable cutoff measure? Do I look at accepted reading assessment tests, such as ...
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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 (Average Treatment Effect on the Treated) using teffects psmatch. I understand the average treatment effect (ATE) is computed by taking the average of the difference ...
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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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In a diff-in-diff or regression discontinuity research design, why is it important to describe why the counterfactual is a plausible one?

I've heard it mentioned that in difference-in-differences, regression discontinuity, or even in some other quasi-experimental research designs, that the counterfactual should be explained as a ...
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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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