Questions tagged [propensity-scores]

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

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Using same control variables as in propensity score matching

I matched my treatment and control group in STATA, using propensity score matching. I also made sure that each treatment/control pair share the same industry and year ( it's a panel data). If I'm now ...
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1answer
11 views

Interpreting ATT in PPM

I am attempting PSM for my observational study. I have created a propensity score, checked balance for treated and controls (using pstest), and used psmatch2 command in STATA. My outcome is mortality (...
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1answer
36 views

Interpreting regression coefficients for covariates after matching

I am new to the fascinating world of matching and propensity scoring. It is highly likely that I will be using some (or more) matching method(s) for my forthcoming project, probably with the R package ...
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70 views

Understanding Propensity Score Matching

I am trying to better understand the motivations and the applications behind Propensity Score Matching. I read the following that explains the motivations behind Propensity Score Matching: Suppose ...
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919 views

Is Propensity Score Matching a "MUST" for Scientific Studies?

Recently, I have been reading about Propensity Score Matching : If I have understood this correctly, Propensity Score Matching is used to construct control/treatment groups in scientific studies, in ...
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3answers
848 views

Do propensity scores reflect the probability of treatment or outcome?

I am a non-statistician PhD student working on a project that has involved some propensity score matching (PSM). I had initially assumed that propensity scores would represent the probability of each ...
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1answer
26 views

Feature Selection and Propensity Score Matching

After reading the section on variable selection in OHDSI for population-level estimation effects, I set out to add additional covariates to my process. As suggested, I began looking at implementing ...
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Should inverse probability weighting be used in two-way fixed-effects panel regression?

Let's assume a (balanced) panel data set with two measurement points $t_0$ and $t_1$, where $t_0$ may be considered as the baseline. Some of the ID's are treated at $t_1$, i.e. $D=1$, the assignment ...
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1answer
262 views

Should I put outcome variable in Matchit::matchit ()

I would like to perform a logistic regression by adjusting for propensity score. My question is, do I have to include the outcome (binary in my case) in the propensity score calculation? Otherwise how ...
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2answers
35 views

Adjust for covariates in small sample size (IPTW, PS match, etc.)

I have a dataset of patients with a grouping variable (groups A (control) and group B (treatment)). The two groups have sample sizes of 170 vs. 30. I would like to compare outcomes between the two ...
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How do I properly run a propensity correction for a fixed-effects model in Stata?

I have a fixed effects regression with a highly unbalanced panel. I am recording the outcome for each day a person participates over the course of a fifteen year time frame. Individuals can enter or ...
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1answer
60 views

Is propensity score matching worse than other matching methods? [duplicate]

Gary King explained why he thought propensity score matching should not be used for matching. See Paper and the Video Lecture. After all these years, have the academics/practitioners reached consensus ...
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1answer
32 views

How to conduct sensitivity analysis on IPSW for survival data?

I am working on survival data, comparing two groups of patients (with or without treatment). These data have some selection bias, thus, I have chosen to weight my sample by Inverse Propensity Score ...
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27 views

What is the prediction model to predict the probability?

In a paper, Dasgupta, 2019 used Difference-in-Difference approach to see whether anticollusion laws implemented by different countries (staggered implementation) affect firms financial flexibility. ...
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1answer
816 views

Inverse probability weighting (IPW): standard errors after weighting observations

When using propensity scores for inverse probability weighting (IPW) the standard errors for the parameters in the regression model may be affected. I have seen several examples of people using ...
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18 views

How to accommodate endogeneity after matching?

I am working on a field experiment where assignment to treatment vs. comparison was random, but participation uptake was not. The design is pre-post, and attrition is certainly not MCAR. This is a ...
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How to calculate propensity scores for multiple treatments with different predictors?

I use propensity score matching with one control condition $d\in\{0\}$ and multiple treatment conditions $d\in\{A, B, AB\}$, where $AB$ denotes the combination of (relatively unrelated) treatments $A$ ...
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1answer
46 views

Treatment and control group balance in ATE estimation with regression

I have a (probably) stupid question but I am still actively learning about causal inference and maybe I am not getting how the pieces do connect together. I came across different methods for ...
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1answer
20 views

When should one use Propensity Score Matching instead of Instrumental Variables to judge the impact?

I have a dataset with a lot of covariates and we need to judge the impact of one variable on the other. Initially, I was supposed to use Propensity Score Matching, but I started wondering as to when ...
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1answer
442 views

Variance of ATE (Average Treatment Effect) from log-linked gamma model

I have matched my sample using propensity score matching such that each individual has an estimated propensity score of being assigned to a treatment group. Let $T_i$={0,1} be the actual treatment ...
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107 views

Derivation of a doubly robust estimator with clever covariate and inverse probability weighting

With notation: outcome $Y$, (binary) treatment $A$, and covariates $L$. In Hernan and Robins (2020) causal inference textbook: To obtain a doubly robust estimate of the average causal effect, first ...
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3answers
73 views

balance in propensity score

I often hear many authors talk about how propensity score helps achieving balance or similarity between treatment groups. Propensity score collapsing information about all the matching variables into ...
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1answer
23 views

Should a variable one is interested in examining for effect modification be used in a propensity model to balance covariates?

Given I have a binary outcome (Y), binary exposure (A), binary potential effect modifier (Z), and a bunch of covariates (C1-C20). We would like to examine for effect modification of Z on the treatment ...
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1answer
78 views

How can I compute standardized mean differences (SMD) after propensity score adjustment?

Standardized mean differences (SMD) are a key balance diagnostic after propensity score matching (eg Zhang et al). Their computation is indeed straightforward after matching. However, I am not plannig ...
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43 views

How would one calculate the p-value from a doubly robust model

How would one calculate the p-value from a doubly robust model I could used the boot.pval r package and set the theta_null (The value of the parameter under the null hypothesis) 0, using student t-...
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1answer
40 views

convert the ATE Average Treatment Effect from a doubly robust model into Odds Ratio

How would one convert the ATE Average Treatment Effect from a doubly robust model into Odds Ratio? Or is it better to discuss this by converting into risk ratio? If so, how is this done.
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What is the result of $\frac{\partial^2 \ell(\beta)}{\partial \beta \partial \beta^T}$?

For logistic regression model, the log-likelihood function can be written as $$\ell(\beta)=\sum_{i=1}^N(y_i\beta^Tx_i-\log(1+e^{\beta^Tx_i}))$$ This is a $(p+1)$ nonlinear equations in $\beta$. To ...
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convert the ATE Average Treatment Effect from a doubly robust model into Risk Ratio

How would one convert the ATE Average Treatment Effect from a doubly robust model into Risk Ratio? Can this be done directly without having to convert to odds ratio first. If so, how is this done. Can ...
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How can I weigh IPTW weight on data

how to calculate manually propensity score weights for multinomial treatments where one of them is baseline From the above question, I wonder how I can modify the original data with weight from IPTW. ...
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1answer
54 views

Why does propensity score matching fail to estimate the true causal effect when OLS works?

Consider the following model (DAG), where D is the treatment (exposure) and Y1 is the outcome. To estimate the causal effect of <...
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20 views

Choosing right value for caliper [duplicate]

I was doing matching using the nearest matching but got stuck with the caliper value. I am not sure which calliper value to choose as many of the tutorials to use 0.1. Suppose, I choose caliper ...
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1answer
31 views

Entropy balancing what are the gains in applying the technique?

I am developing a research that involves a model and when estimating it the coefficients were not significant. Because the hypotheses are strong, my advisor suspects that there is some problem in the ...
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Quasi Experiment with Diff-in-diff but very uneven # of treatment and control sample

Let's say my app has 1M users and I launched a new feature in the app. As a result of this launch 10K users (1%) adopted this new feature. I want to understand the impact of this new feature on ...
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5k views

advantages and disadvantages of IPTW vs propensity score matching?

what are the advantages and disadvantages of IPTW (Inverse Probability of Treatment Weighting) comparing to PSM (propensity score matching) in dealing with confounding variables?
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37k views

Propensity score matching with panel data

I have a longitudinal data set of individuals and some of them were subject to a treatment and others were not. All individuals are in the sample from birth until age 18 and the treatment happens at ...
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60 views

What can I do to get better overlap in propensity score distributions?

I would like to verify the positivity assumption to identify causal effects from observational data. My exposure prevalence is about 6%. When I included several potential confounders in my exposure ...
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1answer
36 views

Does Heckman Model the only method solving MNAR?

I am interested in MNAR (missing data not at random). In some missing data problems, we can use inverse probability of treatment weighting and MLE and multiple imputations. However, it seems those ...
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1answer
125 views

Propensity score matching after imputation in R with mice

9I have a dataset (500 rows) with missing values in different variables for a propensity score analysis. First I created the propensity score matching by omitting the rows with missing values (about ...
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3answers
169 views

Adjusting for a bias between experiment and control before experiment?

Suppose you are interested in how an introduction of some X causes a change in some metric Y in a population. Normally, you would random sample an experiment and control group, introduce the X into ...
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27 views

TriMatch R package gives me equal groups but one of them is larger than original group

I am doing propensity score matching between 3 groups. I am using TriMatch R package and it gives me equal groups (118 in each group) but 1 of them is larger than ...
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1answer
92 views

How does the option"ties" work in psmatch2 in Stata? [closed]

It says in the user's manual : ties not only match nearest neighbor but also other controls with identical (tied) pscores. With neighbor(1) , variables psmatch2 created in our original dataset show ...
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1answer
48 views

Matching with a continuous treatment

I am trying to match a ~60k observation data set, where the treatment (annual days of sun exposure) is continuous. The data also has several confounders, some of which are continuous and some ...
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1answer
52 views

Matching is not recovering the true effect in simulated data

I am trying to recover the true (simulated) effect of a treatment Z on an outcome Y, which is set to ATE = 5 (the csv file for the data is located here: https://www.dropbox.com/s/92obn9hsu3tqy92/...
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Can I use the inverse probability weighting estimators with censored outcome variables?

I am analyzing the impact of financial aid for university students on hours spent working during the academic period by using inverse probability weighting(IPW) estimators ( the reason why I am using ...
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1answer
69 views

More than two genders: Treating gender as factor or logical (for propensity score matching)?

I am matching two samples using a propensity score (I am using the MatchIt package in R as described here: https://www.r-bloggers.com/2016/06/how-to-use-r-for-matching-samples-propensity-score/). One ...
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34 views

How to group cities with similarity in order to perform a regression?

My objective is to understand if the average number of students in the classrooms can lead to better grades in a specific exam to all high school students of the cities in a country. My country has ...
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1answer
429 views

Is IPTW (inverse probability of treatment weighting) legal?

When using IPTW, one can easily get weights 10 or even 20 for the observations. For instance, in logistic regression, weight 10 for an observation means that we have not one, but 10 observations ...
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2answers
675 views

Methods of variable selection

A study stated that it used forward selection to chose variables for a multivariable regression model (in this case logistic) to evaluate association between predictor and outcome. They started with ...
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1answer
208 views

How are weights computed in R matchit() function for full matching?

In the example I have, a small number of treated subjects are matched to a large number of untreated controls. I used ...
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1answer
56 views

propensity score matching with matchit

I am trying to use the package matchit fo propensity score matching on data set (educational stuff) with 52000 observations and a number of variables. For example, I use the command ...

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