Questions tagged [causality]

The relationship between cause and effect.

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4
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1answer
341 views

Consistent estimator - consistent with what exactly?

Lets assume, that the real DGP (real world data) is generated from the model: $$y_i = \beta_0 + \beta_1x_{1i} + \beta_2x_{2i} + \varepsilon_i$$ Lets further assume, that $x_1$ and $x_2$ are correlated....
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369 views

Under which assumptions a regression can be intepreted causally?

First, don't panic. Yes, there are many similar question on this site. But I believe none gives a conclusive answer to the question below. Please bare with me. Consider a data generation process $\...
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19 views

Why does the causal markov condition allow for the interpretation of a bayesian network as a causal diagram?

A related question is here. As far as I can understand from scanning reviews on causal discovery, there are two critical conditions, (1) the causal markov condition and (2) causal faithfulness. It is ...
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1answer
23 views

causal impact estimation

say i have following causal model: outcome variable: Y (e.g. sales) treatment variable: T (e.g. price) covariate variable: x2 (e.g. traffic) unobserved variables: U (unobserved) causal relation: ...
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1answer
26 views

Can propensity score analysis correct for reverse causation or simultaneity

I have data where I suspect there may be reverse causation (Y => X) or simultaneity (Y <=> X). Does the technique of propensity score analysis help to account for this effect? I feel that as ...
4
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1answer
95 views

Do I need to adjust OLS standard errors after matching?

Suppose I use propensity score matching to create a dataset of treatment and control observations. Then I run OLS regression with some covariates that were not necessarily included in the propensity ...
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1answer
38 views

treatment effect on unbalanced panel data

I have an unbalanced dataset that contains movie sales data along with some of the characteristics of the movies for several years. One treatment (event) happened in the society in a specific year in ...
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1answer
16 views

Pick a subset with best (propensity score) matched samples

I am running a matching algorithm to matching patients in a treatment group to patients in a control group without replacement. Say there are $n_T$ treatment group patients and $n_C$ control group ...
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56 views

Linear model: potential outcome framework vs. structural causal model

From my reading about the potential outcomes framework (POF) and structural causal models (SCM), I understand that both perspectives have been shown to be equivalent but take different starting points....
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1answer
21 views

Should you ever use non-bootstrapped propensity scores?

I am trying to measure the difference in continuous $y$ given a binary treatment $B$ and I am using the propensity score matching method. As I built the propensity score model I noticed that small ...
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how to run causalImpact on a time series with multiple interventions?

In CausalImpact package, one defines pre-period and post-period for a single intervention. However, in the scenarios where there are multiple interventions at irregular time intervals, is there a way ...
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Finding the regression coefficients that will remain invariant when an additional variable is added as a regressor

This is Problem 3.8.1(f) in Causal Inference in Statistics: A Primer, by Pearl, Glymour, and Jewell. The above figure implies that certain regression coefficients will remain invariant when an ...
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Computation efficient time series causal graph representation?

I am trying to select the best features to train a Deep Learning model from a big database, consisting of stock/cryptocurrencies prices (and possibly technical indicators). I have found this algorithm,...
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Is there a name for this kind of causal inference from observational data? Using a single model across multiple contexts

Suppose you have two variables A and B, such that A influences B with some coefficient b. What is the correlation between A and B? Well, it depends on the variances of b, so it's going to vary across ...
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1answer
28 views

simple linear regression causality

lets say we have a perfect linear regression, i.e. we have included all relevant variables (to prevent OVB: omitted variable bias), and also such that there is no other problems like mutli-...
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1answer
85 views

Linear regression, good and bad controls, omitted variable error, and causal graphs

This is my first post on this site, and I would really like to thank everyone who engages in this community. I have learned a lot from reading both the questions and answers. My questions are at the ...
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Misspecification in the outcome model with regression adjustment for propensity score?

The propensity score is a popular tool used to control for confounding by covariates $C$ on the effect of an exposure $A$ on an outcome $Y$. There are several ways to incorporate the propensity score ...
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26 views

Intuitive explanation of rules of do-calculus

Do-calculus has 3 rules: https://plato.stanford.edu/entries/causal-models/do-calculus.html I understand them on a mathematical level, but they seem so arbitrary. I can not wrap my head around what the ...
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1answer
17 views

For retrospective observational studies how to determine the baseline of control subjects

I understand that for the active treatment group you can consider the start of treatment as baseline. How about the control group? It is difficulty to do matching without resolving this problem.
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30 views

Why is BART so good?

It seems that BART (bayesian additive regresssion trees) is a very widely and successfully used method for causal inference. However this method was designed as a predictive method and does not ...
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1answer
21 views

Effect of treatment on the treated using the graph: Problem 4.3.2(b) in Causal Inference in Statistics: A Primer

From the above diagram, I need to find the effect of education on those students whose salary is $Y=1$. I was given the hint to use $E[Y_1 − Y_0|Y = 1]$. My attempt: I tried to expand the above ...
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0answers
42 views

Skepticism about the claims of instrument variable validity/exclusion through a statistical test—the Arellano-Bond Test (cross-posted)

I am an applied researcher and occasionally come across papers that have panel data and that use dynamic models with both a fixed-effects term and lagged DV (or multiple autoregressive terms): $y_{it} ...
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1answer
22 views

Testing for causality with Support Vector Machines

Can a support vector machine (SVM) be used to test for causality between 2 or more variables? I know that the original purpose for SVM is classification. I also know that there is a variation of the ...
2
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1answer
32 views

How to determine an appropriate “closeness” threshold when matching for causal inference?

Say I have a [yes/no] treatment variable (e.g. the customer complained about their order) and I want to estimate the causal impact of this "treatment" on the average customer's future spend. ...
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1answer
20 views

Effect of duration of treatment on time to event outcome (overall survival)

I am trying to understand how to design an analysis for the following situation. We have the following data collected retrospectively on a group of patients: Age, Sex, Race, and other variables ...
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Implementations for Conditional Average Treatment Effects that can be trained incrementally

I am currently working on a very large dataset (billions of rows) of A/B test data and want to implement some methods to estimate conditional average treatment effects. I basically need a forecast ...
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1answer
22 views

DAG: when should we use variables marked as “adjusted”?

daggity.net allows to define variables as "adjusted". Manual gives the following definition for adjusted variables: for variables that have been adjusted for in a statistical analysis In the ...
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1answer
28 views

Assessing for causality after genetic matching - how to use weights

I am conducting an analysis of the effect of COPD on particular outcomes after surgery. I have found that utilizing the matchit package with the ...
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1answer
156 views

DAGs: instrumental and adjusted variables

While drawing DAGs, we can define variables as exposure, outcome and unobserved etc. Could you please explain, what are instrumental and adjusted variables?
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1answer
59 views

Regression after matching

I performed 1:1 nearest neighbor matching on 16 covariates using matchit package on R. Covariate balance looks good for most covariates, but there are some that looks less than ideal. I then ran ...
3
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1answer
43 views

Judea Pearl do-calculus using DoWhy package

I have a dataset in which the columns are the variables X,Y,Z,W,A,B. I would like to evaluate $P(Y|do(X=x))$. In the package DoWhy for Python, there is the example: ...
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26 views

Transforming Heterogeneous Treatment Effect Models (in EconML) into Average Treatment Effect Model (from DoWhy)

This question relates to the steps one would need to take in order to reproduce an answer from the DoWhy tutorial, using the EconML library code for heterogeneous causal effects. In DoWhy, there is ...
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1answer
57 views

Is Bayesian estimation useful for causal analyses?

Is Bayesian estimation useful for causal analyses? For analyses like randomized experiments or even observational studies of natural experiments, we want unbiased estimators of the causal effect (...
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0answers
122 views

Regression where predictors are correlated with past values of y

Setup We are interested in estimating a model for the following setup: $Y_t=\beta_0 + \beta_1^{'}X^{'}_t + \epsilon_t$ $COV(X^{'}_t,Y_{t-1,t-2,...,1} | X_{t-1,t-2,...,1}) = 0$ Where $\epsilon_t$ is ...
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1answer
19 views

Recommended books on Mediation Analysis?

I am interested in self learning Mediation Analysis. I have an MSc in Statistics, and I was wondering what would be an appropriate textbook to dig into this area. I would like something that combines ...
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1answer
42 views

Modeling and counteracting exposure bias in recommender systems

I am looking for best strategies to train a new recommendation model from the biased data (due to modeling bias from the previous model). For e.g. Lets assume I have an e-commerce site and initially I ...
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1answer
26 views

Notation for independence of potential outcomes

If we say (binary) treatment status, t is independent of potential outcomes, $\{y_1,y_o\}$, it is usually writing as $t_i \perp \!\!\! \perp \{y_{i1},y_{io}\}$ . I take this to mean intuitively, for ...
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0answers
41 views

Failing to fully control for a variable

Lets assume we want to perform a 'reduced-form' causal analysis to evaluate the impact of a program on the dependent variable of interest. (However the question is more universal). Lets further assume,...
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0answers
6 views

Can I infer causality group of correlated random variables?

I have a group of random variables (vectors of numbers) that are highly correlated $(corr>0.9)$. I wonder if I can infer which of these variables is the dominant one: the one that causes the other ...
2
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1answer
72 views

Causal inference for multiple treatments with an observed set of properties

Note: I have rewritten this question quite a lot, because pzivich's answer made me realize that I had not formulated it accurately enough . In order to give the original context of pzivich's answer, I ...
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2answers
424 views

How can I proceed when causal directions are not that clear? An example is provided

I working with observational data and defining assumptions for DAG seems to be more complex than often in examples provided in textbooks. For me, it would be much easier to just skip DAG part and ...
3
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1answer
69 views

Are causal effects constant over time?

The possibility that correlations are unstable over time is a matter of fact. Just for example we can consider that models included in these articles: https://www.sciencedirect.com/science/article/abs/...
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1answer
69 views

Statistical test to establish that event B is caused by event A

I want to have a statistical test to establish that event B is caused by event A Here event B is customers that are supposed to leaved leave for competitor stay in our company event A is an attractive ...
5
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1answer
62 views

DAG: no back-door paths but background information shows a need for adjusting

I am interested in the effect of town of residence on income. Though the DAG has many arrows, it's interpretation is actually very simple: I have 6 covariates (Cov1-6), all causing mediation ...
2
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1answer
45 views

Aggregate-level difference-in-difference analysis

Thank you in advance. I am new to difference-in-difference (DID) analysis. I want to conduct a research project examining an educational policy shock to local school districts. I have aggregated ...
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2answers
70 views

Difference in Differences - why do we use the terms 'control group' and 'treatment group'?

When doing Difference in Differences, we basically pretend to know the average treated outcome $\frac{\sum_{i=1}^n Y_i(1)}{n}$ and the average no-treatment outcome $\frac{\sum_{i=1}^n Y_i(0)}{n}$ of ...
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0answers
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Do-calculus example from PGM Book by Daphne Koller

Consider the last line of this example. The relevant DAG is shown here. Clearly, $G$ is d-separated from $\hat{S}$ given S, J because the path $G-J-S-\hat{S}$ is blocked by S since arrows meet at S ...
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26 views

When is interventional (do) distribution same to the normal conditional distribution?

I know that generally the interventional distribution is not the same as the normal conditional distribution: $$p(y|\text{do}(x)) \neq p(y|x)$$ But are there non-trivial cases where they are the same, ...
3
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1answer
60 views

Getting the wrong sign on a coefficient in logistic regression?

I'm trying to make a logistic regression model explaining whether a law passed last year has affected my dependent variable. My most important variable (an indicator variable for whether the law was ...
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0answers
17 views

Observational study comparing 2 products with different (but overlapping) feature coverage

I have two software products $A$ and $B$ which form treatment $X$. $B$ is a new version of $A$ and in development. So $B$ does not have all the features that $A$ has. However, $B$ is designed to make ...

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