Questions tagged [propensity-scores]
The probability of receiving a treatment given a set of observed covariates.
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Is the difference-in-difference valid in this case?
I am assessing a report which assess the impact of DBT's export promotion activities in the UK on firm-level outcomes using a combination of propensity score matching and difference-in-differences.
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What are the differences between PS-match and adjusted Cox regression?
This is more like an extension of the following question: Propensity Score Matching with Cox Regression
I am wondering what are the differences between these:
matching patients with PS and running ...
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Risk score developement from admin health data: when the test for the risk factor and outcomes are confounded by indication
I have health records of immunodepressed patients who may have event histories like
[high risk demographics] -> [low lymfocyte count] -> [high viral load] -> [clinical events]
From those data ...
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Propensity score Matching: how to decide if prioritise balance or sample size
I tried different methods (nearest with or without replacement, optimal, full, genetic) to perform PSM between two groups using the MatchThem package. Eventually, I ...
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Propensity Score Matching (PSM) using MatchThem
I am conducting a non-randomized study to assess differences in complication rates between two treatment groups. Before matching, the observed complication rates differ significantly between the two ...
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Incidence influenced by test frequency
I’m puzzled with a study design. Say I want to study a disease incidence, but this disease is asymptomatic mostly and its detection merely relies on regular testing. Some people test more frequently ...
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Primer on Entropy Balancing
I've scoured the internet, but I can't for the life of me, find a comprehensive primer on entropy balancing.
I am currently in the process of cleaning data in order to create weights for the part of ...
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covariate balance loveplot and distance
What is the distance parameter that is plotted on the love.plot ? I am not able to find any reference or explanation for this in the cobalt package. Any help understanding this value "distance&...
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MatchThem and Mixed-Effects Logistic Regression in R
MatchThem and Logistic Regression in R
I am conducting a non-randomized study to evaluate the differences in complication rates between two treatments. The observed complication rates are ...
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Can including a covariate in the outcome model that wasn't included in the PS model introduce bias
Can including a covariate in the outcome model that wasn't included in the PS model introduce bias in the causal effect of the treatment variable?
A colleague shared results of a simulation that seem ...
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Assumptions in the use of propensity scores
A researcher performed a propensity score matching analysis where the exposure was sex (i.e. males vs females). This was not intended to be a causal analysis and the results were described only in ...
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What should it be if the treatment group is larger than control groups in Propensity-Score-Matching (PSM) -DiD
In an effort to do Difference-in-Differences (DiD), to mimic the Randomized Controlled Trials, we normally use the Propensity Score Matching (PSM) to eliminate the difference between control and ...
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Limitations of propensity score matching
While studying propensity score matching, I was struck by the following thought:
When we are running a logistic regression model to estimate $p(Z=1∣X)$ through some form of parametrization and we are ...
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Statistical analyses after propensity score matching with matchit - Survival analysis- adjustment?
For an analysis of a data set I applied propensity score matching (matchit in R, 5:1, without replacement), which went very well- I dod not lose too much of the sample and got a balanced data set out ...
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Modeling approaches for conditional probability distribution, applied to Propensity Score estimation for IPW (causal inference)
I'm trying to understand and ideally implement the Inverse Probability Weighting approach to estimate a causal effect. My ressources so far have been Pearl's Primer and the book "What If?".
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Relevance of balanced covariates for unbiased estimates in OLS regression
In my field of economics, many papers I’ve come across rely heavily on observational data for analysis. However, I've noticed that it's uncommon for researchers to explicitly address whether the ...
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Is multiple comparison correct procedure valid in propensity score matching for different outcomes with different matched covariates?
Let's say I have a large dataset. I have 100 outcomes of interest and for each outcomes, there are associated risk factors, which need not be the same for each of them. However, there is a single ...
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How to determine the treatment time for untreated in difference-in-differences design
I'm estimating the effect of treatment initiation on health outcomes using a Difference-in-Differences (DiD) design. The timeline is defined relative to the treatment date, not calendar time. For the ...
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Propensity score matching - How to select observation for matching among repeated measures
I am unsure how to resolve the following problem and am hoping someone might have a suggestion.
We have repeated measures data for MS patients and we want to look at potential differences in an ...
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In a doubly robust learner, do the covariates need to be the same for the outcome model and the propensity model?
In a doubly robust learner/estimator, do we need to use the same feature set X when creating the outcome model and the propensity model? Or could we use a subset of X for the propensity model or even ...
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(Propensity Score) Matching for multiple outcome variables
I have multiple outcomes (dependent variables) of interest. Before estimating the effect of receiving treatment on these multiple outcomes, I want to use propensity score matching as my control and ...
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Summary and plot after MatchIt for specific patient subgroup
I have used Matchit::matchit() to implement 1-to-1 propensity score matching on a dataset and cobalt::love.plot to show covariate balance for the dataset before and after adjustment.
Is it possible to ...
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Propensity score matching with extremly small data size. Is there any alternative method that works with small data?
I have two groups, A and B, which represent two different imaging methods for detecting a rare disease. This is not a typical treatment-control setup; instead, it involves comparing the efficacy of ...
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Which kind of matching can i use and why doesnt my propensity score matching work?
I am currently trying to find out if a certain disease characteristic affects long term survival. I have observational data and i am trying to match my cohort, as there are huge baseline differences ...
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What are the possible solutions for performing full matching on a dataset with a size of 61,000? [closed]
I am working with a dataset of approximately 61,000 observations (with 27,686 treatment and 32,405 controls) and need to perform full matching using the MatchIt ...
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Use SMD or raw difference in proportions when comparing balance of binary covariates used in propensity matching?
I am a junior in the medical field, and have been tasked with performing a propensity matched analysis of some data, using R.
I am looking at comparing the balance of my covariates (both before and ...
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Issues calculating SMDs after applying IPTW weights
I get the exact same standardized mean differences (SMDs) before and after applying inverse probability of treatment (IPTW) and stabilized IPTW weights to my data. I reproduced this below using the ...
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Why does inverse propensity score weighting work?
Suppose that the effect of some treatment $D = 0, 1$ on the outcome $Y = 0,1$ is confounded by sex $S = 0,1$. An unconfounded estimate of the causal effect of $D$ on$Y$ would see us estimate the ...
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What is the math rationale behind the inverse probability weighting?
Papers say IPTW (inverse probability weighting) is superior to PSM (propensity score matching) because it does not necessarily drop observations, whereas PSM drops those observation not paired.
IPTW ...
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Matching on actual earnings versus matching on the kind of unexpectedness in earnings
For simplicity, lets assume this is a question about linear modeling, although I am actually looking at some non-linear models and am willing to consider other models if they would be more ...
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Propensity score weighting with post-treatment variables
It's been emphasized to balance pre-treatment variables when doing propensity score weighting (PSW) as balancing post-treatment variables can introduce bias.
I want to ask your insights on the case ...
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General question about exact matching on variable/s
I haven't used matching a lot but have certainly read many papers (some quite old and obviously before propensity scores were used much) that have talked about 1:1 matching on age, sex, etc variables ...
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Best Practices for Applying Difference-in-Differences in Panel Data Analysis: Addressing Imbalance and Identifying Suitable Matching Techniques
I am conducting a study on the impact of the African Growth and Opportunity Act (AGOA) on apparel exports from Sub-Saharan Africa using panel data, including 20 treatment countries under the Special ...
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Subgroup analyses after propensity score matching
Assume an already propensity score matched cohort.
One of the variables included in the propensity score model is sex.
The main cox model investigates the comparative effect of two drugs for cancer ...
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In propensity score matching, is the estimand to be estimated the ATE or the ATT?
In the propensity score matching literature (Central Role of the Propensity Score by Rubin), the treatment effect estimand is referred to as the "Average Treatment Effect" (ATE). However, in ...
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Propensity score matching with near-perfect differentiation
I am working on an analytics task for my job to match control units to treated units to monitor the effectiveness of a new marketing initiative. We decided to use a propensity score matching method ...
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What to do with large standardized mean difference in ipw analysis
I'm working on a cohort data, and I'm trying to evaluate the influence from loss to follow-up and missing values. So I performed multiple imputation with chained equation first, then use the imputed ...
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Propensity matching affects significance of polynomial degrees differently
I have a regression as follows:
$$ y = \alpha + \mu L + \beta_1 x + \beta_2 x^2 + \varepsilon $$
where L is a dummy, and x is a control variable.
Both $x$ and $x^2$ are significant when I run the ...
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Does G-computation have any advantages over propensity score based methods for very small sample sizes (e.g., <40)
I am looking into the use of g-computation methods as an alternative for causal inference analysis to propensity score based methods (e.g., IPTW, matching). Does anyone have any examples of using this ...
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Positivity Assumption in Propensity Score Methods for Pre- and Post-Treatment [duplicate]
I am designing a research project and could use some guidance. My research question focuses on estimating the effect of a new co-responder policing program on use-of-force and arrests. I want to see ...
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If I use entire data, the IPW is effective?
When it comes to causal inference, if I use the entire population data, is Inverse Probability Weighting (IPW) still effective?
I have access to the entire population data and need to conduct some ...
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Assessing whether the probability of being assigned treatment is equal (or reasonably close) between two individuals/groups [closed]
I'm currently studying the textbook Design of Observational Studies, second edition, by Rosenbaum. Chapter 3 Two Simple Models for Observational Studies says the following:
3.1 The Population Before ...
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What are the main pros and cons of the most commonly used weighting methods?
There are many methods to generate balancing weights in observational studies (see, for example, the many methods implemented in the amazing WeighIt package).
I have seen some great discussions about ...
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Propensity Score Matching and Weighted Regression Analysis
I have a dataset of ~N=1000 and I want to estimate the average causal/treatment effect of an exposure on an outcome.
I've used propensity score matching to balance baseline covariates, and my matched ...
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Propensity score matching R swap control and treatment
I am analyzing observational data and want to perform propensity score matching. I would like to compare males and females matched for age,smoking status, bmi, etc. When I coded the male gender as 0 ...
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How to deal with missing values in a panel survey (for Propensity score matching analysis)
I would like to know what is the recommended method for data imputation for propensity score matching in panel survey data. This survey has 4 waves and I am examining the treatment effect between the ...
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How to use propensity scores in real examples
I am trying to understand how to use propensity score matching in a real world example (e.g. case control study).
Step 1: Based on what I understand, I think a Logistic Regression is first used to ...
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How to Evaluate Interaction Effects in Propensity Score-Matched Samples:
Suppose I want to study the association between an exposure X and outcome Y, and I have used the propensity score to match each exposed subject with those unexposed but with similar characteristics ...
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Causal inference and Propensity score
I am trying to understand Rubin's causal model but I can not make the connection between certain notions.
The problem of causal inference lies in calculating the counterfactual, i.e. knowing what the ...
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