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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17 views

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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28 views

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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30 views

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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15 views

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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19 views

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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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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60 views

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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12 views

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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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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23 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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37 views

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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57 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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50 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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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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67 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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74 views

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

I am quite new at (quasi) natural experiment analysis, so please bear with me, and got some questions. In my research, I am planning to use one policy change which happened 2002 and treated firms are ...
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18 views

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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7 views

compare time series of different treatments (with pre- posttest data)

I'm having a bit of trouble analyzing some time series for my research project. I have 6 plots in a field, and each plot has 2 water content sensors that measure every 30 minutes. I collected data ...
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Extracting Classifier for Data with Three Distinct Trends

I am investigating a dataset and have found the outcome has three, or maybe four, distinct groups of data with positive correlation. I would like to fit two lines to classify my data points by which ...
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Do I need to control for prognostic variables in a Cox PH model that estimates a treatment effect if the sample matches the target population?

I've been told that if all known prognostic factors are not adjusted for in a non linear model, in this case a Cox PH model, that because the error term is not estimated the treatment variable will ...
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How to model non-experimental continuous individual treatment effect on a binary target?

I have historical data of a problem that can be described as: A person represented by features X has to wait T minutes on a queue so she can receive a treatment (which is equal for everyone), and ...
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107 views

Average treatment effect , binary outcome and odds ratio

I am struggling with ATE. I have two treatment group in a cohort (binary 0=treatment A; 1=treatment B). I want to know the odds of variable Y (binary 0=no event; 1=event) b/w two treatment groups ...
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Use Target Metric or Related Metric for Intervention Analysis?

Say I have a target metric and a second metric related to the target, and I want to measure the effect of an intervention on the target metric. The intervention affects both metrics, but not always ...
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52 views

Difference between the counterfactual mean and average treatment effect

I am new on the causality topic, I don't know the difference between the average treatment effect and counterfactual mean. Can anyone tell me?
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Statistical Analysis of fixed sequence crossover design-placebo followed by medicine

I am planning a fixed-sequence 2-treatment (placebo followed by drug) crossover study. There is a Placebo run-in period of 15 days. Placebo is the control and has no effect on BP. The aim is to ...
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Aggregate effects of randomized experiment

Consider the following two-equation model for observational data from a randomized experiment: (1) $y_{ij} = \alpha + \beta D_{ij} + \gamma X_{j} + \varepsilon_{ij}$ (2) $D_{ij} = \gamma + \delta ...
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Does the regression intercept capture the effect of the treatment year?

I read in a notes that runs the following regression $Y_{it}=\alpha+\beta_1D^{-1}_{it}+\beta_2D^{+1}_{it}+\gamma X_{it}+\epsilon_{it}$ $Y_{it}$ is some health index for individual $i$ in year $t$, $...
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Instrumental variables: In which cases would the average treatment effect on the treated (ATT) and local average treatment effect (LATE) be similar?

It seems that if the proportion of always-takers in the control group (to whom eligibility was not assigned) is much smaller than the proportion of compliers in the treatment group (to whom ...

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