Questions tagged [propensity-scores]
The probability of receiving a treatment given a set of observed covariates.
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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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2answers
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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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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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17 views
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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23 views
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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2answers
34 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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1answer
59 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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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
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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0answers
17 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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2answers
69 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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23 views
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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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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0answers
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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2answers
29 views
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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2answers
44 views
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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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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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
53 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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0answers
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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106 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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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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0answers
9 views
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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0answers
59 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
35 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
124 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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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
90 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
45 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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48 views
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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0answers
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
425 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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1answer
198 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
55 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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0answers
71 views
Causal inference for continuous exposures
I am new to causal inference world and want to find which is the correct statistical procedure that can be applied to my data.
I found a number of predictors 𝑋1...n which are associated with a ...
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1answer
70 views
Large inconsistency between ATE estimates from geralized random forest, propensity score matching etc
I have a RCT data set with about 10,000+ observations and 50+ covariates, and I am trying to estimate the ATE and compare the estimates from a couple of models. The models I use are:
Geralized random ...
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1answer
35 views
DRASTICALLY different sample sizes with categorical data
I am trying to compare the rate of instance of a few variables between two datasets. Dataset A has about 3,000 observations while Dataset B has 180,000. Is it an issue to simply run Chi-Square tests ...
2
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1answer
28 views
Meaning of "65 818 admissions were eligible for matching and 5008 were matched (1:1) in each exposure group" (medical study)
From the abstract of a study that uses propensity score matching to assess the severity of adverse effects of electroconvulsive treatment (ECT):
In propensity score matched analyses, there were 10 ...
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1answer
48 views
Appropriateness of propensity score matching when treatment is determinsitic and exogenous (e.g., COVID restrictions)?
I recently came across this paper by Sibley et al. in which propensity score matching (PSM) was applied to examine the effects of COVID-19 lockdowns on well-being and government attitudes in New ...
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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 ...
2
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0answers
32 views
Difference-in-differences model with a matched control group
I need to run a difference-in-differences (DiD) model, but I'm not sure how to construct a formula for this. The problem is that the timing of events affecting the treatment group is not uniform, like ...
2
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1answer
41 views
Adding baseline covariates in stabilized weights change covariate balancing
I am computing weights using inverse probability of treatment weighting for marginal structural models (Robins et al. 2000). With both time-varying and time-invariant (baseline) covariates, some ...
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0answers
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How to check the effect of a treatment when you do not have control group?
I have a facebook dataset ranging from January-2010 to May-2017 in which the focus of my study are the columns including categories (Group A & Group B), Timestamp, Engagement (Sum of likes, ...
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3answers
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How do I know if my propensiy score estimation is correct?
Are there any test statistics, or visulization methods, or other methods to help me decide whether my propensity score estimates are correct?
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0answers
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matching on weights after multinomial propensity score [duplicate]
I am trying to do an analysis using matching on propensity score weights.
In my data, treatement is a 3-class variable: no treatment, treatment 1, treatment 2.
Outcome is a 2-class variable: 0 or 1
As ...
