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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Estimate Causal Effect of Treatment on Binary Outcome

I am a newbie with zero experience regarding the estimate of causal effects of treatment on binary outcome and this is my first post on stackexchange. I have an unbalanced panel sample of about 5,000 ...
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Doubly robust learning with binary treatment and outcome

I'm trying to use doubly robust learning to estimate heterogenous treatment effects. My treatments T and outcomes y are both binary. I'm following the example listed under "How do I select the ...
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Why isn't the Random Variable over which expectation is taken generally written down or noted as the argument of the expectation?

This is page 266 of the Causal Inference book by Rubin/Imbens. Note that the argument of the expectation is written down below $E_w$ but not in the first set of equations on the page. IMHO, it makes ...
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Doubly robust learning with same features influencing treatment and outcome

I'm looking at some of the examples in the econML package for double machine learning. Specifically, the example found here (code below). In the example W is the features which might influence both ...
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Interpretation of event study difference-in-difference coefficient

I understand mathematically what a difference-in-differences model is estimating, but I want to confirm that I am 'translating' it into English/words properly. Let's say I am running the following ...
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A basic question about ATT and statistical notation

I'm trying to find an answer to what I think should be very basic. It's about the formal equation of the average treatment effect on the treated (ATT): $$E[Y_{1i}|D_i = 1] - E[Y_{0i}|D_i = 1] $$ $Y_{...
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the relationship between ATE and ATT

In my model, I have a consistent estimator for the average treatment effect (ATE). Suppose that $Y^1$ is the treated outcome, $Y^0$ is the untreated outcome, and $D$ is the treatment dummy ($D=1$ ...
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In a RCT, what are the risk of selection bias if no allocation bias is present?

In the Wikipedia page about Randomized Controlled Trials (RCT), the procedures section states that RCT minimize selection bias and allocation bias. My understanding is that selection bias "occur ...
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Including pre-treatment covariates in difference-in-differences: what if my covariates determine treatment but are outcomes of treatment?

I have the following question: What should I do with covariates that affect treatment in the pre-treatment period but are affected by treatment once the treatment is in place in a difference-in-...
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Matching is not recovering the true effect in simulated data

I am trying to recover the true (simulated) effect of a treatment Z on an outcome Y, which is set to ATE = 5 (the csv file for the data is located here: https://www.dropbox.com/s/92obn9hsu3tqy92/...
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Can I use the inverse probability weighting estimators with censored outcome variables?

I am analyzing the impact of financial aid for university students on hours spent working during the academic period by using inverse probability weighting(IPW) estimators ( the reason why I am using ...
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Treatment effect becomes significant after controlling for mediator

I have a question regarding mediation analysis. Suppose X represents the treatment variable, Y represents the outcome variable, and M represents the mediator. When I run the following regression model:...
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Treatment group size vs. Control group size for difference-in-difference estimation

I am reading an unpublished paper that employs diff-in-diff to firm data containing a total of 2598 observations. Of these observations, 2474 are in the treatment group and 124 in the control group. I ...
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How to interpret output from "mediation" package - can I claim full mediation?

I have an outcome binary variable Y, a continuous mediator M, a binary treatment T and some covariates C. I have a linear regression model for the mediator: $ M = \alpha + \beta T + \delta C $ and a ...
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Is the "constant additive unit causal effect" assumption really needed to interpret a regression coefficient as the ATE?

I am reading Unpacking the black box of causality. At page 768 there is written that, in order to uncover the ATE: In observational studies, slightly more complex calculations may be needed, although ...
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Testing in a model with lagged variables and control/treatment groups

Say I conduct regression: y ~ x + t + lag(y) Where y is the independent variable, x is some explanatory variable, t is a dummy variable denoting 1 if the observation is from the treatment group and 0 ...
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How to identify the effects of treatments relative to a control group?

I am currently designing a vignette survey experiment with three conditions. Respondents will either be assigned to (a) the first treatment, (b) the second treatment, or (c) the control group. After ...
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Large inconsistency between ATE estimates from geralized random forest, propensity score matching etc

I have a RCT data set with about 10,000+ observations and 50+ covariates, and I am trying to estimate the ATE and compare the estimates from a couple of models. The models I use are: Geralized random ...
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Difference-in-differences with two policies

Can I use the general DiD approach with two policy variables (i.e., two treatments) as below? xtreg outcome i.policy_1 i.policy_2 i.year, fe vce(cluster id)
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"Bad" control variables in randomised treatment trial

I am analysing the effect of a randomised treatment on several outcome variables. First i am interested in whether the treatment changes the first outcome (non-pecuniary value) by controlling for ...
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No treatment vs treatment A vs treatment B vs both A and B. Is it ok for "both" to be a category?

I want to estimate the effect of some treatments on an outcome. I am interested in the effect of treatment A, the effect of treatment B, and the effect of applying both treatments simultaneously. I ...
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Monthly difference-in-differences with three-way interactions

For my master thesis I am running a set of difference-in-differences (DD) estimators and lately I am a bit confused about the correct specification. I am investigating the differential impact of the ...
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Can I use fixed effects regression with non-time-variant treatment?

I have data on a number of units, some of which have received treatment and some of which haven't. Binary outcome data was collected for each unit at multiple times. The model is therefore $$\text{...
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Counterfactual Estimation - Common Practices in Applied Causality

I am quite new to the topic and trying to figure out a workflow for causal analysis. My aim is to establish a baseline of ATE (I think) and then experiment with disentangled representations and ...
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What does "sharp design" mean in Chaisemartin (2020)'s paper compared to Goodman-Bacon (2017)'s paper?

When studying the paper of Chaisemartin (2020) comparing to Goodman-Bacon, 2017's paper regarding heterogeneous treatment effects, a commentator said that Chaisermartin's papers have more of a sharp ...
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Variance of difference in mean estimator for Average Treatment Effects

Consider $n$ Patients each of whom is treated with probability $p$ and given control with $1-p$ independent of everything else. Let $W_i$ be $1$ if the patient $i$ is treated and $0$ otherwise. Let $...
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Relationship Between Binomial and Poisson Regressions to Get Treatment Effects

Let's say I'm at an e-commerce company running an experiment with a binary treatment and want to understand the % lift the treatment gives to products on average. A typical way to handle this might be ...
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MCMC fitting of Dirichlet Process or Polya Tree prior to residuals in (simple linear regression)/(2-independent-samples) problem

Consider a simple location-shift semi-parametric model with two mutually-independent samples (in what follows, $F$ is a cumulative distribution function (CDF) on $\mathbb{ R }$, the $C_i$ and $T_j$ ...
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Can Hedge's g statistic be used to measure the difference between two experimental groups?

I understand that Hedge's g statistic measures how much two groups of samples differ. And it is used between an experimental group and the control group. But, I want to know if it is suitable to know ...
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What should we conclude when the significance levels are different but the signs are similar among coefficients on a variable of interest?

I assessing the impact of anti-collusion laws on dependent variables $Y_{it}$ across the country by using generalized DiD. The identification is: $Y_{it}$ = $\alpha$ + $\beta$ $(pt)_{kt}$ + $\delta$$...
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Why is the generalized DiD not reported as the Variance Weighted Average Treatment Effect on the Treated (VWATT)?

Normally, when dealing with the staggered implementation of laws we normally use generalized difference-in-differences. I saw that Dasgupta, 2019, p. 2596-2597 did not mention the VWATT which is ...
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Standard error for population average treatment effect with nearest neighbor matching

I perform nearest neighbor matching where each treated group $i$ (where $i$ goes from 1 to $I$) has a population $n_i$ and is matched with a non-treated group with population close to $n_i$. The ...
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How to get the weights of coefficients of treatment effect from De Chaisemartin,2020?

Thanks to the suggestion from @Ariel in this discussion, I visit this paper and face a problem. The DID equation is $$Y_{i,g,t} = \alpha_g + \alpha_t + \beta D_{g,t}+e_{igt}$$ $Y_{i,g,t}$ is the ...
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Which one of these methods ATE, ATT, ATO (overlap) should be used to evaluate a strategically applied treatment?

Lets assume I have data from a medical trial in which a new medicine was given to patients who, based on certain characteristics, doctors believed that they would benefit from this new treatment. I ...
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Proof: Succesful matching reduces model dependence

I am reading through a paper regarding propensity score matching (Gary King and Richard Nielsen. 2019. “Why Propensity Scores Should Not Be Used for Matching.” Political Analysis, 27, 4, Pp. 435-454. ...
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A doubt on different ways of performing staggered DID?

Recently, I read two papers by Dasgupta,2019 and Dong,2019 examining the impact of staggered laws in different dependent variables. When having a really long discussion with people in the comment ...
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Why do not we include post and treatment variables separarately in generalized DID?

In a generalized Difference-in-Difference, Dasgupta, 2019 using this equation $Y_{it}$ = $\alpha$ + $\beta$ $(Leniency Law)_{kt}$ + $\delta$$X_{ikt}$ + $\theta$$_t$ + $\gamma$$_i$ +$\epsilon$$_{it}$ (...
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AB test with interaction between observations in the two groups

Suppose I am the owner of an e-commerce store. I'd like to design an AB test to measure the average effect of discounts on sales. I am not able split user traffic so that two users see different ...
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Including outcome only related covariates in propensity score estimation

When estimating the propensity score in an observational study, there seems to be a relative consensus on the fact that covariates related to the treatment and the outcome (i.e. cofounders) and ...
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Why we need at least three pre-treatment time periods for a two-year difference-in-difference model?

From reading a discussion in this forum, I am wondering what is "two-year difference-in-difference". From my understanding, the basic DID is we examine the change of treatment compared to ...
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How can I deal with left censoring on continuous outcome variables

I have a Y variable that is long term (ex: profit in the next 12 months). This means that I only fully observe Y only in cohorts that are at least 12 months old, but I can partially observe it in the ...
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Interpretation of diff-in-diff with race dummy

I am having an argument with a teaching assistant over the interpretation of this model: \begin{aligned}WAGE_i=&\alpha+\beta_1*FEMALE_i + \beta_2*BLACK_i + \left(\beta_3*FEMALE_i \times BLACK_i\...
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WeightIT package error: treatment and covariates must have same number of units [closed]

While using the weightIt package in R I encountered a strange error: "error: treatment and covariates must have same number of units" Now, checking the root code of the package and this ...
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Correct methodology using $g$-computation to estimate Average Treatment Effect on the Treated ($ATT$)?

I have a question about the $g$-computation methodology for estimating the Average Treatment Effect on the Treated ($ATT$) in the following article. The authors recommend estimating the $ATT$ by first ...
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Instrumental Variable and "Exclusivity"

In the following DAG: Can I use IV1 as an instrument for exposure? In the this video at 4:26 the teacher explains a principle of "exclusivity" for instrumental variables. Cutoff causes ...
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Naive Estimator after Propensity Score Matching

I wanted to compute a Naive Estimator after a Propensity Score Matching with full matching. I came up with this code but I'm not sure. How can I compute the ATT using a Native Estimator after PSM? <...
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Is different distribution of confounders across groups a problem for OLS as this researcher says, and if so which assumptions are violated?

I am watching this video where the presenter writes (the link will open at the correct time): Regression models Problems [...] confounders have different distributions across the intervention groups ...
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What are the use cases for Propensity Score Matching?

I have asked here whether, in order to establish causal relationships, the treated group and the control group must be similar on all covariates. The answer was no, if we control for the covariates in ...
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Discover causal effects using OLS: does the treated and not treated group need to be similar on all observed variables?

I have one dummy variable, $D$, which equals 1 if the subject received treatment and $0$ otherwise. My outcome of interest is $Y$. For example, $D$ tells me whether the subject took the drug or a ...
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What are some examples when the Average Treatment Effect on the Treated/Control (ATT,ATC) is more sought after than the ATE?

I am wondering when and why one may calculate the Average Treatment Effect on the Treated (ATT) or the Average Treatment Effect on the Control (ATC). Is there a specific example or motivation for when ...

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