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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Estimating the effect of an endogenous treatment using IV, when treatment probability is heterogeneous

I am interested in studying the effect of an endogenous event ($D\in\{ 0,1\}$) on outcomes ($Y=\beta'X + \alpha D$) (I want to study both continuous and binary y). The probability of this event ...
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The average treatment effect and the difference in means

Hi I have a question related to the treatment effect. Recently, I am reading literatures on treatment effect and have a question. In the literatures, we denote the counterfactual outcomes as $Y_1$ and ...
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Covariate for consistent estimation of treatment effect

I come across this sentence as I was reading journals: "to consistently estimate the treatment effect, any covariate X introduced must have equal expectations in treatment and control: E[X|Di= 1] =...
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difference-in-difference: Dynamic treatment group/timing

I want to use difference-in-difference (DiD) to estimate a treatment effect. However, my problem is a little different from the standard DiD application in that: The items in the treatment group may ...
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Time Treatment Effects Insignificant with Baseline Interaction - Linear Mixed Effects Model

I am running a linear mixed effects model in R. I am testing the effects of psychedelics on well-being. I have a time treatment effect (everyone took psychedelics in this cohort, there is no control). ...
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Literature Tips: Interaction Effects Typically Smaller than “Main Effects” (in econ/soc-sci)

I am looking for literature dealing broadly with the hunch pretty much everyone has in econ/social sciences: main effects of a treatment are typically larger than interaction effects. Of course, this ...
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Regression Model to obtain ATET

I'm working on a problem where I have two groups that both received a different treatment and one controlgroup. The total number of people in this experiment is 10,000 and the participants where ...
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Sample Inclusion Criteria for an Intent-To-Treat Analysis in an Encouragement Design With One-Sided Non-Compliance

This question is about how to define the sample in an Intent-To-Treat analysis of an experiment with balanced pre-assignment and an encouragement design. This is a bit different than the usual setting ...
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Invariance of average output given output maximization

Assume that here are two areas $a = {1,2}$ and that $(e_{i1},e_{i2})$ is IID Gumbel location 0 scale 1 for all $i$. Assume further that $$w_{i1} = \mu_1 + e_{i1} \\ w_{i2} = \mu_2 + e_{i2}$$ and that ...
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After using PSM to select a sample of treated and control subjects from a larger population: would I be estimating the ATE or ATT?

Suppose I use propensity score matching to select a sample of treatment and control subjects from a larger population for a follow-up survey. Assuming I matched subjects using the full range of p-...
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Bacon decomposition

I have a panel dataset in which treatment happens over time. I am using a two-way fixed effects model to estimate the effect of a dummy treatment variable. I am trying to check whether my DID ...
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Identifying assumptions of Difference-in-Difference versus Fixed Effects models

I am reading the seventh edition of Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge and I am a bit confused on the different identifying assumptions for difference-in-difference ...
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Estimating LATE from RDD using OLS - Have I understood it correctly?

I am currently running a project using RDD in STATA where I am unable to use the handy "rdrobust" command, and hence have to use the conventional "regress" function instead, i.e., ...
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Control group within the country for national policy

I have a question about control group selection. I want to evaluate a private education suppression policy. The policy is that 70% of questions of a "national" university entrance test is ...
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Regression or statistical test for change in counts after a treatment

I am seeking suggestions for suitable regression or statistical tests to measure the effect of a treatment in counts (or proportions), when a control group is not available. Let's say an event takes ...
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How to calculate and interpret a marginal treatment effect (local instrumental variable)? (Intuition through simple example.)

I am working on the intuition behind local instrumental variables (LIV), also known as the marginal treatment effect (MTE), developed by Heckman & Vytlacil. I have worked some time on this and ...
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Estimating the Local Average Treatment Effect (LATE) and always-takers

I am new to statistics and am particularly interested in RCTs so this is a very basic question. If I have a program wherein 10% of my control group had access and used the treatment, how would that ...
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Panel data 'binary' treatment with multiple 'odd' start and end times

I am investigating the impact of a county-level policy on crime outcomes. I am using a two-way fixed effects estimator. My exposure (i.e., treatment) is a static binary variable. The binary treatment ...
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Regression coefficient for a logged dependent variable

I am running into a puzzling econometric issue. Here is my data set-up and the model: I am using a two way fixed effects model on a panel data of states. For regression results, I take natural log of ...
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Interaction variable or additional variable

I want to expand the following baseline model to investigate the effect of time: $$ price_{it} = \alpha + X_{it} + treat_{it} + \epsilon_{it} $$ My data is pooled cross section and I distinguish for 2 ...
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Is the difference in change an unbiased estimator of the treatment effect in a longitudinal randomized controlled trial?

Suppose I am analyzing a randomized controlled trial with a pre-post design, and I want to estimate the effect of a treatment on a continuous outcome variable. According to this paper by Twisk et al. (...
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59 views

Should the treatment effect coefficient in a difference-in-difference model be consistent with graph trend?

I am running a difference-in-difference regression and the coefficient of the interaction term (treat*post) is negative, so I concluded that the effect of the treatment was negative on the outcome ...
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Why is BART so good?

It seems that BART (bayesian additive regresssion trees) is a very widely and successfully used method for causal inference. However this method was designed as a predictive method and does not ...
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Bimodal posterior of ATE predicted via Bayesias Additive Regression Trees

I am using BART to estimate the ATE on a large (10k obs x 224 p) dataset with a binomial outcome. In short, I first model the risk of the outcome $Y$ given the $(Z,X)$ covariates, then, for a chosen ...
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Propensity score matching and unbalanced co-variables in IPWT

When using propensity score (PS) for calculating inverse probability weighting (IPW) in an average treatment effect (ATE) approach, is it valid to remove from the PS those co-variables that remain ...
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Implementations for Conditional Average Treatment Effects that can be trained incrementally

I am currently working on a very large dataset (billions of rows) of A/B test data and want to implement some methods to estimate conditional average treatment effects. I basically need a forecast ...
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Cluster issues for treatment effects analysis

I'm doing a Difference in Differences exercises on 32 regions within a country to test for the effect of a policy intervention. However, only 1 of those regions has been subject to the intervention; ...
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Transforming Heterogeneous Treatment Effect Models (in EconML) into Average Treatment Effect Model (from DoWhy)

This question relates to the steps one would need to take in order to reproduce an answer from the DoWhy tutorial, using the EconML library code for heterogeneous causal effects. In DoWhy, there is ...
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Is Bayesian estimation useful for causal analyses?

Is Bayesian estimation useful for causal analyses? For analyses like randomized experiments or even observational studies of natural experiments, we want unbiased estimators of the causal effect (...
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Causal inference for multiple treatments with an observed set of properties

Note: I have rewritten this question quite a lot, because pzivich's answer made me realize that I had not formulated it accurately enough . In order to give the original context of pzivich's answer, I ...
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How to calculate Cohen's d between multilevel model predictions at two points in the domain?

I would like to get a Cohen's d between model predictions at different points in the domain of a mixed effects model. I have model predictions and I have bootstrapped standard errors. I mention the ...
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26 views

Average treatment effect from matrix of individual posterior distributions

I'm trying to estimate the average treatment effect of an intervention using the potential outcomes framework in a classification problem. The analysis uses machine learning to learn $\hat{y} = f(Y, X,...
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Average Treatment Effects Over Time

I'm testing whether people trust advice more from source A or source B. Trust in advice in a categorical variable with 4 values. Each subject answered ten questions, so I have ten observations per ...
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Analysis Ideas for Unusual Observational Time Series w/Treatment

I am studying some retrospective time series survey (panel) data, sometimes with an intervening treatment, and would like feedback on an analysis concept to test for a treatment effect. Each ...
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Propensity score matching - What is the problem?

In estimation of treatment effects a commonly used method is matching. There are of course several techniques used for matching but one of the more popular techniques is propensity-score matching. ...
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166 views

Diff-in-Diff time varying treatment

I would like to estimate a Difference in Difference specification where the treatment (i.e., the policy change P) is off then on and then off again: ...
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Why are my coefficients too large when control variables are not added?

I am doing a difference in differences analysis with staggered treatment time. Since the treatment time is different among subjects, I made my matrices look something like this from this post (Dynamic ...
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24 views

Fixed effects with panel data vs including lagged variables with cross section data

I have panel data with many groups $i$ and two time periods $t$. I want to know the effect of a binary treatment $D$ on a continuous outcome $Y$. Some groups go from untreated to treated, while others ...
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37 views

Aggregation Estimation Issues

I'm analyzing the effect of a law enforcement measure over time on reported violence for districts in a city. If I consider the city as a whole I have access to a lot of possible control variables (e....
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Is there a joint test of model coefficients, effectively testing for main and interaction effects in quantile linear mixed model?

I am just reading this paper: Linear Quantile Mixed Models: The lqmm Packagefor Laplace Quantile Regression. Let us assume I have a repeated observation experiment, where I want to assess the effect ...
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What is the best way to model when the treatment is intermittent across years?

Let's say the goal is to gauge the effect of deploying certain clean technology (dummy variable: 1 for the year the technology is deployed, 0 otherwise) on a firm's pollution level (continuous ...
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68 views

Winsorizing propensity scores

Is it kosher? Inverse propensity weights (IPW) has been shown to perform poorly when selection probabilities are small (Kang and Schafer, 2007). Are there any standard solutions to this issue?
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Extending external validation diagnostics to experiments with continuous treatment

Does the external validation diagnostic methods discussed in Stuart et al. (2011) (i.e., inverse propensity score weighted regressions) also apply to the experimental setting in which the treatment is ...
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63 views

Cox proportional hazards model with inverse probability treatment weights: testing the Cox proportional hazards assumption

I have survival data of persons who participated/didn't participate in a health promotion program. To estimate the effect of the health promotion program on the hazard of mortality, I plan to use a ...
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Strong ignorability: confusion on the relationship between outcomes and treatment

In the research area of potential outcomes and individual treatment effect (ITE) estimation, a common assumption called ''strong ignorability'' is often made. Given a graphical model with the ...
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Estimating policy intervention effect for comparison groups but no control

I have panel data in 6 waves for land parcels that include 11 covariates that are known to affect the outcome of interest (probability of becoming a protected area). Six of the covariates are time-...
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1answer
38 views

Conditional treatment effects for RCTs

I have randomized controlled trial data with one control and two treatments. Since the DV is continuous, I estimate the treatment effect using an lm model, while ...
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78 views

How to compute the Quantile Treatment Effect?

Consider you have defined a statistical model for the potential outcomes $Y_i(0)$ and $Y_i(1)$ for each experimental unit $i=1,\cdots, N$, as in Rubin (1978)'s model-based inference for causal ...
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162 views

How is it decided to use Difference-in-Differences, Regression Discontinuity or both together (if possible) according to a policy change?

I am quite new at (quasi) natural experiment analysis, so please bear with me and my questions. In my research, I am planning to exploit a policy change which happened in the year 2002. Treated firms ...
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Correct test for before and after samples of binary counts?

TL;DR --- for $n = 1,\dots,N$ counties, we have the numbers $x_n$ and $y_n$ of STI+ and STI- people for two different years. Between year 1 and year 2, a small number $M$ of the counties closed ...

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