Questions tagged [propensity-scores]

The probability of receiving a treatment given a set of observed covariates.

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Paired or unpaired tests after using propensity score matching [closed]

I used propensity score matching in one of my studies to reduce the effects of confounding. Although some authors have suggested that methods of inference appropriate for independent samples can be ...
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Propensity Score Matching: Can I use this test to compare the coping strategies for health shock between Vietnam and India compared to comparison test

I want to investigate the coping strategies employed by households during health shock in Vietnam and India. In place of regular chi square or independent sample t test, I want to use PSM by ...
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Hypothesis testing in moderation analysis after Propensity Score Matching

I would like to perform a moderation analysis after Propensity Score Matching with a binary outcome model. I estimate a logistic regression outcome model (following this vignette). I am wondering, ...
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Variance of Influence Functions, Cross-fitting, and the Propensity Score

Following example 2 in this paper, suppose I wanted to estimate $\psi = E[E[Y|X,A=a]] $ and I had an influence function follows: $$ IF(\psi) = \frac{A}{\pi(X)}\{Y-\mu(X)\} - \psi $$ where $\pi(X)$ is ...
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SMR-weighted model to estimate ATT

I am using SMR weights in a logistic regression model. Everyone in the exposed group gets a weight of 1, and everyone in the unexposed group gets a weight of propensity score/(1-propensity score). ...
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Dimensional Reduction on multi dependent variable for propensity score matching

I have an analysis project and I am not quite sure about my analysis plan. The data context are about the treatment effect with confounding variables is well-established. But it have many dependent ...
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Best Approach to Detecting Breakpoints in Continuous Treatment-Outcome Relationship?

I am currently conducting research to understand the relationship between a continuous treatment variable and a continuous outcome variable. To mitigate confounding and selection bias, I am employing ...
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Using exact same covariates in CBPS model and regression model

When using CBPS in R and following Imai and Ratkovic, do you necessarily need to include all the covariates in your covariate-balancing procedure that you are planning to use in the subsequent ...
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Propensity Score Matching on Pre-treatment Covariates

I plan to do a propensity score matching and then do a Difference-in-Difference design. I'd like to only match using pre-treatment covariates. However, when I use the MatchIt package in R, I could not ...
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How to calculate p-value from non-inferiority study after OR?

I am conducting a study in which I test two different treatments A and B. The outcome is binary (success, fail). We want to demonstrate if treatment B is non-inferior to A with a margin of the ...
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Using SPSS Propensity Score Matching yields Propensity Scores but no Match IDs

I'm using the IBM provided PSM module in SPSS 26 on MacOS. I have 1 group indicator and 4 covariates (3 scalar-1 nominal). I tried match tolerances of 0.5 and 1. I tried with and without replacement ...
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Treatment Effect Insignificant with OLS but Significant After IPTW

I'm working with some observational data and wanted to assess the effect of variable (D) on outcome (Y) after controlling for a vector of covariates (X). As the data is observational, I wanted account ...
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How to use Kernel weighted PSM and IPW in Triple Difference estimator (ika Difference in Difference-in-Differences)

I have learned a lot from @dimitriy answer in this post: Propensity Scores Weighted DID I am currently utilizing the triple difference estimator for estimating treatment effects (please see the paper ...
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Pairwise censoring for survival analysis in a 1:x propensity-score matched sample

Take e.g. an observational study sample where subjects have varying follow-up lengths. We want to compare two treatments, A (intervention treatment; n=100) & B (control treatment; n=500), and see ...
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Is there relationship between propensity score based causal inference and sampling weights?

Consider observational study with single outcome $Y$, single covariate $X$ and treatment assignment variable $W$. Under unconfounded treatment assignment assumption, $E_{sp}[Y(1)]=E[\frac{Y_i^{obs}W_i}...
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Do the weights of IPTW have to sum up to the population size?

I'm new to Inverse Probability of Treatment Weighting and trying to understand the mechanism. From several practical guides I understood, that the weights should add up to the population size for ...
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Propensity score paradox and propensity score matching

Propensity score paradox and propensity score matching I went over the papers by King and Nielsen (2017) and Ripollone et al (2018) to figure out what is propensity score paradox in propensity score ...
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Interpreting a coefficient for a matched/weighted data set for non-linear outcomes

Prior research has demonstrated that the interpretation of a regression coefficient no longer represents the marginal effect once non-linear term transformations (logs, interactions, etc.) are ...
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Propensity scores: Variable K:1 matching with caliper- some technical questions (MatchIt, cobalt)

I have a data set from a cohort comparing two treatmens which I want to balance via propensity score matching. I read some literature and decided to use a variable K:1 matching because this seems to ...
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Log-Rank Test After Optimal Pair Matching

I have clinical data and used optimal matching in the MatchIt package to match cases to controls on several variables. Matching was done in a 1:1 ratio, and balance was achieved. I then did a Kaplan-...
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What is the difference between the average treatment effect obtained from Propensity Score Matching and the Hazard Ratios?

I performed a moderation analysis according to the point "moderation analysis" https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html Now, I have found that my ...
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When analyzing causality from X to Y, is IV regression better than Propensity Matching Score

When we are analyzing the causality from X to Y, especially when literature has used extensively IV regression and PSM as a method. However, as I know PSM is matching method while IV is a regression ...
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Estimation of Propensity Score using Random Forests

Suppose that one has a binary treatment $Z$, and assume that $Z=1|X=x \sim Bern\left(e(x)\right)$. Furthermore, suppose I want to estimate the propensity score by a random forest. Are there ...
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can someone explain the application of row.weights and sampling.weights in Lavaan?

I am trying to include the weight derived from Inverse Probability of Treatment Weighting into a structural equation model.However, it is unclear how it should be incorporated into the model and how ...
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Difference between normalized difference and standardized mean difference in cobalt?

In Imbens & Wooldridge (2009, p. 19), they define the normalized difference as: whereas the cobalt's package standardized mean difference uses by default (for the ATE) "the 'pooled' standard ...
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Propensity Score Matching at the district level and selecting variables

I am trying to use PSM for program evaluation. My data is at the individual level and I would like to do the PSM at the district level (match districts with each other rather than individuals). Based ...
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R SurvSplit survival with Propensity Score Matched groups

I am looking to perform a SurvSplit of the survival r-package. The difficulty that I have is that I would like to perform the SurvSplit on two matched groups (full Propensity Score Matching). So I ...
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How do I pair matched data after propensity score matching?

I used matchit package for propensity score matching. I got another column under names of "distance", "weights" and "subclass". What I understood is "subclass" ...
suyeon kim's user avatar
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Formula for standardized mean difference in Cobalt package for categorical variables

I am having troubles in understanding the formula in cobalt package used for standardized mean difference calculation in BINARY variables ...
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How do I do McNemar test for propensity matched data in R?

I did propensity score matching for retrospective data, and now I want to run outcome analysis. For dichotomous variables, I heard that I need to do McNemar test since I should treat propensity score ...
suyeon kim's user avatar
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Discrepancy between standardized mean difference in cobalt and smd packages

Let's suppose I have weighted my population using WeightIt ...
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PSM Propensity Score Matching Combination of exact and full matching

I would like to compare two groups (one treatment group and one control group). In both groups one can identify three different subgroups (let's call them Index-subgroup). I would first like to ...
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Extracting matched sample dataframe from MatchThem

I am using the R package MatchThem to match a dataset that contains 19 control samples and 13 case samples. After running the ...
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Solution when matching on $X_1$ increases imbalance in $X_2$ and vice versa?

My work team has come across a situation in our propensity score matching analysis where matching treatments to controls on one covariate, $X_1$, increases the imbalance in a second covariate, $X_2$, ...
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How can I get the most balanced groups using matchit in R?

I have an imbalanced treatment group and a control group. I want to use Matchit in R to calculate propensity scores and get balanced groups. However, there are several parameters I have to set in the ...
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Confounding variables are nested with treatment, not able to be measured, how to address the influence from confounding factors?

We gathered driving data from two cohorts of drivers belonging to the same age group. The first cohort, Group A, utilized System A (treatment group), whereas Group B drove vehicles without this system ...
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Log-Rank test with propensity score matched data

Let's assume that I have two groups that I would like to compare in terms of death rates. One group (group A) got a specific treatment and the other group (group B) did not get any treatment. I ...
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How can you rewrite the estimand in terms of propensity scores? Dowhy question

I am going through the backdoor criterion and how we get from an expression involving do to one which doesn't as below. What i don't quite get is how to rewrite this estimand in terms of propensity ...
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How does the psmatch2 command increase sample size in Stata?

I first ran a regression with my dependent variable and covariates: reg dv treatment covar1 covar2 covar4 covar5 covar6 The n=738 Then a used psmatch2 to generate new treatment/control groups ...
education 's user avatar
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When I try to use PSM, Should I calculate for normal distribution with Shapiro-Wilk normality test before pair matching or after pair matching?

Suppose there is dataset of 100 patients' including the item "Blood pressure". 45 patients belongs to Group A, and the rest of 55 belongs to Group B. In consequence, there will be 30 vs 30 ...
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Is it normal to have different results pattern if data sort is different but the covariances are the same when doing propensity score matching in R?

Is it normal to have different results pattern if data sort is different but the covariances are the same when calculating with propensity score matching in R? There is data with sample size of about ...
nan's user avatar
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Invalid use of Propensity Score Matching?

I wonder if using a propensity score in the following situation is wrong. Imagine I have the next causal model $$X = N_x$$ $$Y = f(X, N_y)$$ $$ Z = g(X, Y, N_z)$$ Where $N_z, N_y, N_x$ are ...
Nicolas Beltran's user avatar
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how to understand the weights in PSM?

When using propensity score matching or weighting, a column of weights is generated that is used to estimate the effect of interest. According to a blog I read, there are three types of weights ...
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How to Evaluate and Visualize the Positivity Assumption for the ATT

I am trying to formally evaluate and visualize the satisfaction of the positivity assumption when estimating the ATT using R and I am having a tricky time figuring out how to do so. As Greifer and ...
Brian Lookabaugh's user avatar
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Is this sample size big enough to analyze with Propensity Score Matching?

Suppose I have a dataset where 9 patients occured with the post-operative complication. (e.g. information such as height, smoking, weight, age, disease status) and rest of the 150 patients without the ...
nan's user avatar
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About two way fixed effect

Question: Suppose there is a job training program rolled out in Georgia from 2011 to 2015 . The aim of the job training program is to provide work experience for a period of 12 to 18 months to ...
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Using Propensity Score Matching to Reduce "Class Imbalance" Biases?

Suppose I have a dataset where 100 patients have the disease (e.g. information such as height, smoking, weight, age, disease status) and 10000 patients do not have the disease (i.e. class imbalance). ...
stats_noob's user avatar
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Calculate risk ratio in weighted population

I have a propensity score weighted population (using IPTW) and I want to compute risk ratios on my weighted population. For this, I am using a weighted Poisson regression. Let's suppose that "...
user19745561's user avatar
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matchit: Specified caliper still matches everyone!

Suppose we are using the matchit function from the MatchIt package in R, as in the following example given in the package, ...
Victoria's user avatar
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Unbalanced variables after IPTW - entropy balancing?

After using inverse probability of treatment weighting (IPTW) on the variables of my dataset, there is still an imbalance in one covariate between the two groups. My outcome is binary (yes/no) and it ...
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