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Questions tagged [propensity-scores]

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

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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 ...
Iram shahzadi's user avatar
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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 ...
Joseph Kletzer's user avatar
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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 ...
eshuns's user avatar
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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 ...
sunny_aus's user avatar
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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 ...
gweny17's user avatar
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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 ...
Demetri Pananos's user avatar
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Meta-learner trained on matched data [closed]

I am trying to estimate the average treatment on the treated. I have used propensity score matching first, to create the control and treatment groups. I end up having quite small group sizes (1500 ...
gummy's user avatar
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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 ...
Tom Hsiung's user avatar
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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 ...
andrewH's user avatar
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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 ...
HYL's user avatar
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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 ...
beza afework's user avatar
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2 answers
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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 ...
geek45's user avatar
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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 ...
user321627's user avatar
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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 ...
Emma's user avatar
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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 ...
YYM17's user avatar
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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 ...
Babak Fi Foo's user avatar
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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 ...
brookskieran's user avatar
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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 ...
galaxy-friday1017's user avatar
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1 answer
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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 ...
user1190107's user avatar
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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 ...
The Pointer's user avatar
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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 ...
Charly Marie's user avatar
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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 ...
J2019's user avatar
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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 ...
user18802819's user avatar
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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 ...
user23960363's user avatar
1 vote
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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 ...
user avatar
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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 ...
zjppdozen's user avatar
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1 answer
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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 ...
Guillaume's user avatar
5 votes
1 answer
393 views

How to derive the GMM estimator for the Covariate Balancing Propensity Score?

The covariate balancing propensity score (CBPS) described by Imai and Ratkovic (2014) involves fitting a logistic regression for the propensity score $\pi_\beta(\mathbf{X}) = P(T = 1\vert\mathbf{X})$ ...
Noah's user avatar
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Is a MSM with no lagged values equivalent to simply using IPW to balance a data set?

I am working on a project employing a panel data set with a large N but a fairly small T (5 time periods). MSMs seem like a good strategy, but I am wary of the incorporation of lagged values since ...
Brian Lookabaugh's user avatar
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Propensity score matching - within subsample analysis

I am trying to understand the impact of a graduate degree in mathematics on the wages of students compared to other degrees. I have been running a probability score matching model using a logarithm ...
Giulio Cavallari's user avatar
2 votes
0 answers
52 views

IPW Weights for Marginal Structural Models for Different Estimands

As Blackwell and Glynn 2018 note, an interesting property of marginal structural models is their ability to estimate treatment effects that account for the dynamic properties of panel data. For ...
Brian Lookabaugh's user avatar
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1 answer
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Do I have to use the exact same variables in each step, if I have a two-step propensity score match followed by a regression?

I am using the propensity score match to grow my sample based on a smaller dataset of existing units that received the treatment. The match will find more likely units from its large population that ...
LifelongLearner2's user avatar
2 votes
1 answer
90 views

How to obtain the (weighted or unweighted) L1 imbalance measure for "raw" data

I have two questions regarding the JASA paper, Multivariate Matching Methods That Are Monotonic Imbalance Bounding, by Iacus et al. (2011), the authors produce Figure 2 (left panel) where they compare ...
yungmist's user avatar
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Propensity score matching for differently sized control and treatment groups

I would like to use propensity score matching to construct a post hoc control group to accompany a pre-determined treatment group. Some literature I am seeing says that I should pairwise match sample ...
Leonhard Euler's user avatar
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After creating a weighted dataset using IPTW, can we use the same covariates again during regression in Survival analysis? [duplicate]

I'm conducting an observational study between two non-randomised treatment groups. I plan to balance the two using IPTW (inverse probability of treatment weighting). I'll be using some covariates (...
Uro_Star's user avatar
5 votes
1 answer
35 views

Should I use independent- or propensity score matching?

I work on data form an observational study (n=34) and want to compare the outcome of the individuals to an historic cohort (n=600). Subjects in the observational study received additional treatment ...
Hans Dak's user avatar
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Marginal Treatment Effects using MTEFE - postestimation discrepancy

I am currently calculating Marginal Treatment Effects for an outcome Y (earnings) and a treatment D (joining sector 1 vs sector 0), using the MTEFE package from Stata. I use a separate approach with a ...
Clara HL's user avatar
1 vote
0 answers
15 views

How can I estimate ATT for each stratum [closed]

the following code if for estimating ATT, my question is :if I want to estimate ATT for each stratum,can I use this code and how can I change it to give me ATT for each stratum? ...
alireza beheshty's user avatar
1 vote
0 answers
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estimating effect with marginaleffect package

I want to estimate ATE. first of all I used MatchIt package for full matching for propensity score and then I used logistic regression with all of variable in propensity score model after that I used ...
Mahboobeh Taherizadeh's user avatar
1 vote
0 answers
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Estimating treatment effect with G computation

I used the following code to obtain ATE. Before using the 'marginal effect' package, I examined the coefficients of the model and noticed that the 'symptom' variable has a high standard deviation. Is ...
Mahboobeh Taherizadeh's user avatar
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0 answers
26 views

Robust Difference-in-Differences: What is the propensity score doing?

Callaway and Sant'Anna (2022) describe a doubly robust difference-in-differences (DiD) method that is attractive in staggered DiD (multi-group treatment times) for several reasons. It can prevent &...
dcoy's user avatar
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0 votes
1 answer
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How can I use propensity scores to manual match cases and controls before I begin data extraction?

I am conducting a retrospective case-control study, it is a clinical project. Initially I manually matched cases to controls of the same age and sex. It had been suggested that to have a less biased ...
user404553's user avatar
1 vote
0 answers
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How to estimate interpretable treatment effects using a marginal structural model?

Say that I estimate a marginal structural model with weights obtained by inverse probability weighting. Imagine that my model looks something like: $Y_t = X_t + X_{t-1}$ again, with observations ...
Brian Lookabaugh's user avatar
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0 answers
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When to calculate Outcome value in Propensity Score Matching with staggered adoption?

Sorry if this is a basic question (I'm no statistician): In a propensity score matching study, WHEN do you calculate the outcome values (for each treated and control unit) needed to compute the ...
Romain's user avatar
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1 vote
0 answers
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Does propensity score matching reduce OVB?

Suppose you have data on a large population, a small proportion of which is treated. Let's assume there are enough treated data points that you don't need propensity scores for data reduction. There ...
Mohan's user avatar
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2 votes
1 answer
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Matching order based on grouping variable

I want to match in a certain order. I am aware of the argument order, available in package {MatchIt} - but this refers to the ...
geek45's user avatar
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1 vote
0 answers
36 views

Coefficient covariance matrix of inverse probability weighted regression

I am interested in computing an estimate $\hat\Sigma_\hat\beta$ of the asymptotic covariance matrix of the parameter estimates $\hat\beta$ in a regression of $Y$ on $\{X, Z\}$, weighted by weighs $\...
Noah's user avatar
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3 votes
3 answers
242 views

Can you compare Propensity Score Matching estimates?

I am considering a research project involving Propensity Score Matching (PSM). For a high-level overview, I am looking at college students. Colleges offer need-based, and merit-based scholarships. I ...
shrey_shankar's user avatar
2 votes
1 answer
54 views

Causal inference - propensity score balancing sufficient for potential outcome balancing?

I am trying to make some causal inference estimates in a dataset and was hoping someone here could help me out with a question I have coming out of my background reading. It seems that a very ...
justin's user avatar
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