Questions tagged [causality]

The relationship between cause and effect.

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Propensity Score Matching – How do the mechanics lead to a different result than unmatched?

The gist of propensity score matching, as I understand it, is as follows: You want to estimate the average treatment effect (ATE) of a treatment on some outcome. However, if you simply calculate the ...
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8 votes
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Why is BART so accurate in causal inference?

The famous paper Dorie,2017 shows that BART performs dramatically well in causal inference. In my replication, MSE in BART can be 40% lower than MSE in other machine learning methods. But all machine ...
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7 votes
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Avoiding adjustments for time-varying controls in difference-in-differences (DID)?

In difference-in-differences (DID) analysis, it seems like a "folk theorem" that one should be very wary of adjusting for time-varying controls. The reason, eminently plausible, is that ...
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Machine Learning for Causal Inference with Panel Data: Possible to combine ML estimators with additive/linear terms to derive diff-in-diff estimator?

My question is motivated by the following. First consider the non-panel case, where we have two groups, the treated group ($g=t$) and the comparison group ($g=c$), and are trying to estimate an ...
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7 votes
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Difference-in-difference in panel data

Under which conditions should we expect the difference-in-difference estimate to be equal to the equivalent panel data model? Strictly speaking, whenever we have a experiment that offers a well ...
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6 votes
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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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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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Before using CV-selected Regression model for Inference, shouldn't model performance be evaluated on unused test set?

I just came across a biokinesiology paper that used some Machine Learning methods, but I think there is a flaw in their methodology. The authors had data on stroke patients and used Lasso regression ...
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6 votes
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316 views

Structural equation and causal model in economics

Structural equations is a useful language for causal analysis in economics. In Causality Pearl (2009 cap 5) we can find the best discussion about this. My question: is possible to use the concept of ...
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Inverse Propensity Score Weighting vs. Double Machine Learning

I am familiar with Inverse Propensity Weighting (IPW) for the estimation of causal effects, and recently, I came across the 2016 paper by Chernozhukov et al. on Double/Debiased Machine Learning. From ...
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Counterfactual Estimation - Common Practices in Applied Causality

I am quite new to the topic and trying to figure out a workflow for causal analysis. My aim is to establish a baseline of ATE (I think) and then experiment with disentangled representations and ...
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128 views

Conterfactual estimation in machine learning model

There are various techniques to build counterfactual estimations of certain variables for linear models in observational studies. Some of those are based on comparing the change in the predicted ...
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How can I control for time-varying unobserved heterogeneity with panel data?

I'm just beginning to learn econometrics, and I just learned about the fixed effects estimator, and the first-difference estimator. It's quite straightforward that those techniques allow me to control ...
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Ranking of causal models

I remember seeing a paper that ranked causal statistical models, published by some research body, by quality in terms of generalizability or some similar facet(s). This would be a "hierarchy of ...
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Causal inference, stratification to mitigate confounders in continuous variables?

Handling confounders in continuous variables In Statistical Rethinking, the author shows that in different situations, a confounder (fork, pipe, collider, descendent) will induce spurious correlations....
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Causal modeling and DAGs in Python - where to start and what are the best sources?

I am very new to causal models (and econometrics) and need to pick up basics fast. I am comfortable with ML though. I did an extensive research during last several days on causality, DAGs, and ...
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4 votes
1 answer
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Comparing time series data with multiple pairs of time series, or difference-in-difference with continuous treatment conditions?

My dataset contains time-series for two variables ($X$ and $Y$) from 2017 to 2020, for each of many different countries. Each country has its own time series for each variable (X_usa, X_india, Y_usa, ...
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4 votes
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Applying Heterogeneous treatment effects to clinical research (non technical explanation)

I'm trying to understand the hype around this estimation of heterogeneous treatment effects in the machine learning literature lately. It seems super interesting, but alot of it is beyond me. I read ...
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How is EMSE derived for causal trees in Athey and Imbens (PNAS 2016)?

Athey and Imbens build a non-parametric matching procedure to identify and estimate causal effects. To this end, they minimize the expected mean squared error (EMSE) of their procedure, but I don't ...
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131 views

Are model diagnostics necessary for linear model run on matched data?

On https://cran.r-project.org/web/packages/cem/vignettes/cem.pdf, it mentions that "Using the output from cem, we can estimate SATT via the att function. The simplest approach requires a weighted ...
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Causality in Time Series

I am reading an article which is trying to justify the need for causal inference in their inferential framework. The thought experiment is as follows: Suppose a statistician is asked to design a ...
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Adjusting for experimentally-caused panel attrition when evaluating treatment effects

This question involves a questionable hypothetical scenario, but please bear with me. Suppose I ran an experiment in a coffee stand where the treatment was playing country music instead of the usual ...
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Policy announcement as a treatment variable (causal inference)

I am using data from sub-reddits like [this][1] or [this][2], where users discuss their thoughts on the Federal government unemployment insurance and its fairness. Specifically, I wonder if it makes ...
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3 votes
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how to perform double ML with binary data (either in the treatment or in the outcome)?

I have grown interested in double Machine Learning (ML) for causal inference because it answers an intuitive question: if the relationship between a variable $X$ (the treatment) and a variable $Y$ (...
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Isolating Effects in a Causal Model

I am working on trying to recover correct group means in a causal inference model (I'm pretty new to causal inference) that I'm running on a simulated dataset. I believe that the issue I'm running ...
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3 votes
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35 views

how to verify whether the observed data follows an additive noise model

Many causal discovery methods assume the data follows an additive noise model. My question is, given an observed dataset, is there a way to assess the assumption? How to tell if the observed dataset ...
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3 votes
2 answers
150 views

Machine learning for causal inference

I have a multiclass classification problem where the target variable is actually different categories of causes, and the dataset is observational. I know of causal inference, and I would like to learn ...
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Does the variance of regression coefficient in multiple linear regression increases if we replace one of the predictors with its immediate parent?

We have a data generation process as follows: \begin{align} Z_1 &= E_{Z_1}\\ Z_2 &= \theta_{Z_2} + \theta_{Z_1 Z_2} Z_1 + E_{Z_2} \label{Z2BasedOnZ1}\\ T &= \theta_{T} + \theta_{Z_1 T} ...
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60 views

How can you determine whether there is concept drift or whether a model is affecting the distribution of the target class?

Assume that I am building a churn prediction model, and I collect observational data of customers who registered in the last 12-18 months. Assume that 50% of customers churned. Customers who are ...
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3 votes
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86 views

All-subsets regression and parameter shift to estimate or identify omitted variable biases?

I have multiple ($12$) predictors ($X$) for an outcome (spending) where it's likely/possible that: Some predictors are correlated Some predictors could (partially) mediate the effect of others There ...
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Reverse Causality with Additional Period

I have been struggling for a model to estimate related to sequential treatment effect and need a help desperately. I would greatly appreciate it if you guide me to the resources or advice me on this ...
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3 votes
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34 views

How to find the [marginal] effect of X on Y when Y is binary and very rare. Can I make groups of similar X and model counts of Y instead?

tl;dr How to model the causal impact of X on binary Y when Y is very rare. Can I make groups and model count instead? Background/What I tried I want to know what the effect of the number of "...
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3 votes
0 answers
52 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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56 views

Extending external validation diagnostics to experiments with continuous treatment

Does the external validation diagnostic methods discussed in Stuart et al. (2011) (i.e., inverse propensity score weighted regressions) also apply to the experimental setting in which the treatment is ...
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3 votes
1 answer
161 views

Causal tree v. causal forest - when to use which for HTE?

Would someone be able to explain the considerations for using a causal tree versus a causal forest to estimate heterogeneous treatment effects? Is it that a causal forest is less prone to overfitting? ...
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1 answer
53 views

Measuring the causal impact of a policy that is not binding

This may be a little tricky because it's difficult to explain but bear with me. Assume a new policy implemented in 2015 which is a new requirement for firms, let's say for instance that the ...
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3 votes
0 answers
312 views

Mathematical details in the definition of a Structural Causal Model

Pearl defines (see Causality, Judea Pearl, 2nd ed., Definition 7.1.1) a Structural Causal Model (SCM) as a triple $(\mathscr U, \mathscr V, F)$ where $\mathscr U$ is a set of "exogenous variables," $\...
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3 votes
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521 views

Coefficient of Ratio + Ratio of Coefficients: Inconsistency

I have regression results about the effect of a treatment on two outcome variables, $N$ and $D$. The coefficient on $N$ is positive. The coefficient on $D$ is negative. These results suggest that the ...
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30 views

Causality in variance with a BEKK model

I am using a BEKK model in the following form, $$H_t=C^\ast{C^\ast}^\prime+\sum_{i=1}^{m}{A_i\varepsilon_{t-i}\varepsilon_{t-i}A_i^\prime+\sum_{j=1}^{s}{B_jH_{t-j}B_j^\prime}}$$ I first start with a ...
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471 views

How to relate roots of AR and MA to unit circle

I'm working on these problems and think I figured out most of the steps, but am stuck near the end as I don't understand how to relate my roots back to the unit circle in order to determine ...
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800 views

Pro and cons between Bayesian structural time series (BSTS) vs difference-in-differences?

Google's paper markets BSTS's benefits over DID such that "In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of ...
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  • 293
3 votes
1 answer
311 views

Invariance of causal prediction

I am reading Causal inference by using invariant prediction: identification and confidence intervals by Jonas Peters (link to the resource is here: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/...
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3 votes
1 answer
2k views

Relation between AR(p) stationarity and causality

Let's take an AR(p) model $\phi(L)y_t=z_t$ where $\phi(L)=1-\phi_1-...-\phi_pL^p$ and L is the lag operator. I have just studied that if there are no roots of the polynomial on the unit circle, $1/\...
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3 votes
1 answer
375 views

Structural Equation Model and Causality in Economics

I would want to make a study about the influence of some regressors in the evaluation of the effects of increment of subsidy in an economic sector. I would use SEM (Structural Equation Model) to ...
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3 votes
0 answers
36 views

What simple test-case and corresponding solution, would demonstrate what it is possible to achieve using causation/precedence-analysis techniques

What would be minimal test-case(s) and corresponding solution, demonstrating curent know-how in causation/precedence analysis solving, or more simply what is it possible to achieve using causation/...
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3 votes
0 answers
1k views

Marketing/Sales Mix/Response Models: approaches and comparisons

CV/SO Community: I am probably skirting (or crossing) the line of the preference for questions that can be answered vs. those that can (only) be discussed. That said, I'm trying to wrap my head ...
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3 votes
0 answers
121 views

When does the Rubin Causal Model fail in practice?

The Rubin Causal Model frames the causal inference question as the problem of inferring missing potential outcomes (what the outcome would have been if a unit had received a different treatment) in ...
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596 views

reverse causality when dependent variable and independent variable are observed on different levels

Consider the following: $$ Y_{ijt} = \gamma \cdot P_{jt} +X_{ijt} \beta+\epsilon_{ijt} $$ The j dimension is only indicated for clarity, the regression is a panel regression on i-t dimensions. The ...
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539 views

Estimating a confidence interval around the average of an interacted treatment effect

I've got a causal inference situation with heterogeneous treatment effects. In addition to simply estimating coefficients, I'd like to get an average effect of the treatment at the covariate values ...
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57 views

Guessing test question answers from scores

My teacher likes to give online quizzes that are about 20-30 questions long. Every student has the same questions in the same order. We are not told after taking the quiz which questions we got wrong, ...
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